system

Abstract

Provide a system. A means for receiving emails; A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories; A means for automatically generating a task list based on the analyzed email content; A way to monitor task progress and receive reminders about tasks that are approaching deadlines, means for displaying and updating the task list through a user interface; A system including:

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In business, the amount of information received via email is extremely diverse, and it is necessary to manage this information appropriately and quickly extract and execute necessary tasks. However, traditional methods require manually checking the contents of each email and creating a list of tasks, which takes time and effort, making this process difficult to do efficiently. Furthermore, there is a lack of mechanisms to properly manage task progress and ensure reminders for tasks with approaching deadlines, which can lead to work delays and important tasks being overlooked. There is a need for a system that can solve these problems and improve business efficiency by integrating email and task management. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving emails, a means for analyzing received emails using natural language processing technology and classifying the email contents into specific categories, a means for automatically generating a task list based on the analyzed email contents, a means for monitoring the progress of tasks and sending reminders for tasks with approaching deadlines, and a means for displaying and updating the task list through a user interface. Furthermore, the system includes a means for setting a person in charge and a deadline based on the analysis results of received emails, and a means for categorizing emails into categories such as requests for quotes, inquiries, order procedures, and schedule adjustments based on the analysis results. This system automatically manages emails, efficiently and accurately extracts necessary tasks, and appropriately sends reminders, thereby achieving work efficiency and thorough task management.

[0006] "Means for receiving email" is a function for receiving email sent from outside and storing it within the system.

[0007] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate language that humans use on a daily basis.

[0008] The "means for classifying the contents of emails into specific categories" is a function that analyzes the text of received emails and classifies them into predetermined categories based on their contents.

[0009] "Means for automatically generating task lists" is a function that automatically creates necessary tasks in list format based on the analyzed content of emails.

[0010] "Means for monitoring task progress" refers to a function that tracks the implementation status of each task in real time and identifies the status of completion and incompleteness.

[0011] The "means for reminding the user about a task whose deadline is approaching" is a function that warns or notifies the user when the deadline for a set task is approaching.

[0012] A "user interface" is a collection of visual and manipulable screens and input devices that allow a user to interact with a system.

[0013] "Means for setting the person in charge and deadline" is a function that assigns a person responsible for a specific task and sets a deadline for the task. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0036] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database.

[0037] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body, and classifies the email content into specific categories. Examples of categories used in this classification include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0038] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate person and deadline. For example, in the case of a "quote request" email, a task called "Create a quote" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt.

[0039] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0040] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0041] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0042] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are ensured to be completed without being overlooked. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0043] The above is an embodiment of the present invention.

[0044] The processing flow will be explained below.

[0045] Step 1: Receiving email

[0046] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0047] Step 2: Analyzing the email

[0048] The server analyzes the text of the stored emails using natural language processing technology. It extracts important keywords and phrases from the email body and classifies the content into specific categories. In this process, categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment" are used.

[0049] Step 3: Creating tasks

[0050] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote," sets the person in charge to a sales representative, and sets the deadline to three days after receipt. The generated task is added to the task list.

[0051] Step 4: View the task list

[0052] The terminal displays the task list to the user. The user can check the task list through the user interface. Details of each task, the person in charge, the deadline, and other information are displayed.

[0053] Step 5: Update the task

[0054] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0055] Step 6: Remind

[0056] The server monitors the progress of the task list and sends reminders for tasks that are approaching their deadline. For example, a reminder email is sent to the user one day before the task deadline, saying, "The quotation creation is incomplete."

[0057] The above are the specific processing steps of the program of the present invention.

[0058] Example 1

[0059] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0060] Traditional email and task management systems required users to manually analyze email content and create and manage tasks, which was inefficient. Manually monitoring task progress and managing deadlines was also cumbersome, potentially resulting in work delays. Furthermore, there was a lack of consistency and accuracy in classifying tasks into specific categories, leading to the risk of important tasks being overlooked.

[0061] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0062] In this invention, the server includes means for receiving emails, means for analyzing received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for assigning appropriate personnel and deadlines to each task, means for monitoring the progress of tasks and sending reminders for tasks approaching deadlines, means for displaying and updating the task list through a user interface, and means for using a generative AI model in natural language processing technology. This enables automatic analysis of email content, highly accurate and consistent categorization, and task generation and management. Furthermore, the reminder function ensures thorough deadline management and prevents work delays.

[0063] "Means for receiving email" refers to a device or software that has the function of periodically receiving new email from a mail server.

[0064] "Natural language processing technology" refers to methods and technologies for analyzing natural language and extracting meaning from text data, and examples include NLP libraries and models.

[0065] A "means for classifying into specific categories" is a device or software that has the function of analyzing the content of received emails and classifying them into preset categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment."

[0066] The "means for automatically generating a task list" refers to a device or software that has the function of listing the necessary tasks based on the analyzed email content, and allowing the system to automatically create the task list.

[0067] The "means for setting an appropriate person in charge and a deadline" is a device or software that has the function of assigning an appropriate person in charge to a generated task and setting a corresponding deadline.

[0068] The "means for monitoring the progress of a task" refers to a device or software that has the function of continuously monitoring the progress of a generated task and grasping the current status.

[0069] The "means for sending reminders" refers to a device or software that has the function of sending reminder notifications to the person in charge when the deadline for a task approaches.

[0070] The "means for displaying and updating a task list through a user interface" refers to a device or software that displays a task list on a user terminal and allows the user to change or update the progress status of tasks through the interface.

[0071] A "generative AI model" is an artificial intelligence model that realizes various data analysis and generation functions, including natural language processing, and specific examples include models such as GPT and BERT.

[0072] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0073] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze. This conversion and saving process uses, for example, Python's IMAP library, and saves the received emails in a local text file or database.

[0074] The server then analyzes the text of the stored emails using natural language processing (NLP) techniques, such as the NLTK library or spaCy. These tools extract important keywords and phrases from the email body, and based on the results, classify the emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment." Generative AI models (such as GPT and BERT) are also applied to improve the accuracy of the analysis.

[0075] Based on the analysis results, the server automatically generates a task list. This task list is generated according to pre-set rules. For example, in the case of a "quote request" email, a task called "Create a quote" is automatically generated, assigned to a sales representative, and the due date is set to three days after receipt.

[0076] The generated task list is displayed on the user's device through a user interface. The user interface can be a front-end application built using React or Vue.js, for example. Through this interface, the user can check the task list and update the progress of each task. For example, when a user completes the "Create a quote" task, they can mark the task as "Complete."

[0077] The server also monitors the progress of tasks and sends reminders for tasks that are approaching their deadlines. For example, an email sending script is used to send a reminder email to the user one day before the task is due, saying "Your quotation is not yet complete."

[0078] Examples and prompts

[0079] Specific examples

[0080] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0081] Prompt Sentence Examples

[0082] "Receive a new email and classify it into a category called 'Request for quote' based on its content. Next, create a task called 'Create quote' and set the assignee and deadline. Then, display the created task in the user interface."

[0083] This invention integrates email management and task management, automatically analyzes email content, and converts it into appropriate tasks, ensuring that important tasks are not overlooked and are carried out reliably. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0084] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0085] Step 1:

[0086] The server accesses the mail server to check for new mail and receives it.

[0087] Input: Request to access mail server

[0088] Data processing: Retrieve new emails from the mail server using the IMAP or POP3 protocol.

[0089] Output: Retrieved email data

[0090] Specific operation: The server uses Python's IMAP library to connect to the mail server and retrieve new emails. The retrieved emails are temporarily stored in memory.

[0091] Step 2:

[0092] The server converts the received email into text format and stores it in a format that is easy to analyze.

[0093] Input: Received email data

[0094] Data processing: Analyzes the HTML and MIME structure of the email and extracts the text portion.

[0095] Output: Email data converted to text format

[0096] What it does: The server extracts the text of the email and saves it to a local text file or database, along with metadata such as the sender and the date and time the email was received.

[0097] Step 3:

[0098] The server analyzes the text of the stored emails using natural language processing (NLP) techniques.

[0099] Input: Email data in text format

[0100] Data processing: Using inductive analysis techniques, important keywords and phrases are extracted.

[0101] Output: Extracted keywords and phrases

[0102] What it does: The server uses the NLTK library and spaCy to parse the text and extract key keywords and phrases. Optionally, it also leverages generative AI models (e.g., GPT and BERT) to improve the accuracy of the analysis.

[0103] Step 4:

[0104] The server classifies emails into specific categories based on the extracted keywords and phrases.

[0105] Input: Extracted keywords and phrases

[0106] Data crunching: Applying classification algorithms using keyword matching and machine learning models.

[0107] Output: Categorized email data

[0108] How it works: The server uses SciKit-Learn's clustering algorithm to classify emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment."

[0109] Step 5:

[0110] The server automatically generates a task list based on the analysis results.

[0111] Input: Categorized email data

[0112] Data processing: Convert into task list format and set the necessary information (task name, person in charge, deadline, etc.).

[0113] Output: Generated task list

[0114] Specific operation: The server generates tasks according to the category of the email based on pre-defined rules. For example, if an email is a "quote request," a task called "Create a quote" is automatically generated, and a person in charge and deadline are set.

[0115] Step 6:

[0116] The server transmits the generated task list to the user terminal.

[0117] Input: Generated task list

[0118] Data processing: Convert the task list into an interface format.

[0119] Output: Task list data sent to the user's terminal

[0120] Specific operation: The server acts as the backend system of the web application, retrieving the generated task list from the database and sending it to the frontend.

[0121] Step 7:

[0122] The user terminal displays the task list through a user interface.

[0123] Input: Task list data sent from the server

[0124] Data processing: Convert the task list into a display format

[0125] Output: Task list displayed on the user interface

[0126] Specific operation: The user device displays a task list in dashboard format on a front end built using React and Vue.js.

[0127] Step 8:

[0128] The user can view the task list and update the progress of each task through the interface.

[0129] Input: Task list on the user interface

[0130] Data manipulation: Updating task progress

[0131] Output: Updated task list

[0132] How it works: Users can change the status of each task to "in progress" or "completed" in the interface, and that information is updated in the database in real time.

[0133] Step 9:

[0134] The server monitors the progress of tasks and provides reminders for tasks approaching deadlines.

[0135] Input: Task list and progress information in a database

[0136] Data calculation: Check deadlines and extract tasks that need reminders

[0137] Output: Send reminder email

[0138] What it does: The server uses a periodic job (e.g. a Cron job) to check the task progress and deadline information from the database, and generates and sends reminder emails to users.

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] In logistics centers, tasks requested via email (e.g., shipping preparation, inventory checks, inspection work, etc.) are often managed manually. This results in tasks being overlooked or delayed, leading to problems with reduced work efficiency. Furthermore, there is a lack of real-time feedback that allows employees to work efficiently. To resolve these operational inefficiencies, a system is needed that can automatically analyze the content of emails and generate and manage tasks based on that analysis.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for monitoring the progress of tasks and sending reminders for tasks with approaching deadlines, means for displaying and updating the task list through a user interface, and means for displaying the task list on a smart device and providing visual feedback on the update status in real time. This enables automation of task management in logistics centers, preventing task oversights and delays and significantly improving work efficiency. By allowing employees to work while checking the progress of tasks in real time on their smart devices, work efficiency is improved and errors are reduced.

[0144] "Means for receiving email" refers to a device or system that has the function of periodically receiving new emails from an email server.

[0145] "Means for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories" refers to a device or system that analyzes the text data of received emails, analyzes the content using natural language processing technology, and classifies the content into appropriate categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment, shipping preparation, inventory check, inspection work, etc.).

[0146] The "means for automatically generating a task list based on the analyzed email content" refers to a device or system that has the function of automatically generating corresponding tasks in list form based on the results of email analysis.

[0147] A "means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching" is a device or system that has the function of monitoring the progress of each task included in a task list in real time and sending reminder notifications when the deadlines are approaching.

[0148] A "means for displaying and updating a task list through a user interface" is a device or system that provides an interface that allows a user to visually view a task list and update the status of each task.

[0149] "Means for displaying a task list on a smart device and providing real-time visual feedback on the update status" refers to a device or system that has the function of displaying a task list on smart glasses or other portable digital devices and providing real-time visual feedback on the progress of tasks.

[0150] The present invention is a system designed for efficient task management in a logistics center. This system has the function of automatically analyzing received emails and generating and managing a task list based on the analysis. The following describes in detail an embodiment of the present invention.

[0151] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database. The server then analyzes the saved email text using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body. As a result, the email content is classified into specific categories. Categories used in this classification process include "quote request," "inquiry," "order procedure," "schedule adjustment," "shipment preparation," "inventory check," and "inspection work."

[0152] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate assignee and deadline. For example, for a "Prepare for shipment" email, a task called "Prepare for shipment" is generated, with the assignee set to logistics staff and the deadline set to three days after receipt. The generated task list is displayed through a user interface on the smart device (e.g., smart glasses). The user can check the task list and update the progress of each task through the interface. For example, when a user completes the "Prepare for shipment" task, they can mark the task as "Completed."

[0153] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, a reminder notification stating "Shipping preparation work is not yet complete" can be sent to smart devices one day before the task deadline. Employees can work while checking the progress of their tasks in real time, improving work efficiency and reducing errors.

[0154] This system is implemented using the following hardware and software. The hardware includes smart glasses and a server. The software includes Python, imaplib, email, sklearn, and spacy. The server uses this software to receive emails, analyze text, generate tasks, and send reminders. The smart glasses display a task list in real time and accept input from the user.

[0155] As a concrete example, if a logistics center manager receives an email saying, "Please prepare for shipment," the system automatically analyzes the email's contents, generates a task called "prepare for shipment," and displays it on the smart glasses. Employees can then confirm the task, prepare for shipment, and report completion to the system through the smart glasses. This automates task management and enables efficient business operations.

[0156] Example prompt sentence:

[0157] "Please prepare shipment for new orders."

[0158] "Please check stock availability"

[0159] "Please inspect the new shipping list."

[0160] The above is an embodiment of the present invention.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] The server periodically accesses the mail server and receives new mail.

[0164] Specifically, the server connects to the mail server using the IMAP protocol and searches for "unread mail," resulting in a list of new mail.

[0165] Input: Mail server account information, IMAP protocol

[0166] Output: List of new emails

[0167] Step 2:

[0168] The server converts the received email into text format and saves it in a format that is easy to analyze.

[0169] Specifically, the email body is extracted and saved in a text file or database.

[0170] Input: List of new emails

[0171] Output: Plain text of the email body (saved file or database entry)

[0172] Step 3:

[0173] The server analyzes the text of stored emails using natural language processing (NLP) techniques.

[0174] Specifically, the server uses the spacy library to parse the email body, extract important keywords and phrases, and classify the content into specific categories.

[0175] Input: Email body in plain text format

[0176] Output: Important keywords and phrases, categorization results

[0177] Step 4:

[0178] The server automatically generates a task list based on the parsed email content.

[0179] Specifically, the server generates appropriate tasks based on keywords and categories, and assigns a person in charge and a deadline to each task.

[0180] Input: Category classification results, keywords

[0181] Output: Auto-generated task list (task name, assignee, due date)

[0182] Step 5:

[0183] The server displays the task list on the smart device and provides visual feedback on the update status in real time.

[0184] Specifically, the server sends the task list to a smart device (e.g., smart glasses) so that the user can check it. When the task progress is updated, the information is displayed in real time.

[0185] Input: Task list

[0186] Output: Task list display on smart device

[0187] Step 6:

[0188] The user views the task list and updates the progress of each task through the interface.

[0189] Specifically, the user updates the task completion status through the smart device, and the updated information is sent to the server, which then updates the task list again.

[0190] Input: User task progress update

[0191] Output: Updated task list

[0192] Step 7:

[0193] The server monitors the progress of tasks and reminds you about tasks that are approaching their deadlines.

[0194] Specifically, the server monitors the deadline for each task in the task list, and when the deadline approaches, it generates a reminder notification and sends it to the smart device.

[0195] Input: Due date for each task in the task list

[0196] Output: Remind notification (notification to smart device)

[0197] The above are the specific processing steps of this system.

[0198] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0199] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0200] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored in a text file or database.

[0201] The server then analyzes the text of the stored email using natural language processing (NLP) technology, which extracts important keywords and phrases from the email body and classifies the content into specific categories. Examples of categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0202] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and a deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list.

[0203] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data.

[0204] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. One method is to send reminders when the user is relaxed, or, conversely, to raise the threshold when the user is highly stressed.

[0205] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0206] In addition, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "Your quotation is not yet complete."

[0207] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0208] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are not overlooked and are reliably progressed. Furthermore, by adding a user emotion recognition function using an emotion engine, task priorities are adjusted and reminders are set according to the user's state, thereby reducing workload imbalances and stress.

[0209] The above is an embodiment of the present invention.

[0210] The processing flow will be explained below.

[0211] Step 1: Receiving email

[0212] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0213] Step 2: Analyzing the email

[0214] The server analyzes the text of the stored emails using natural language processing technology. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. In this classification process, categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment" are used.

[0215] Step 3: Creating tasks

[0216] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote." The person in charge is set to a sales representative, and the deadline is set to three days after receipt. The generated task is added to the task list.

[0217] Step 4: Receiving emotion data

[0218] The device collects data from the camera and microphone to monitor the user's emotional state in real time. The emotion engine analyzes this data and recognizes the user's emotional state (happiness, anger, surprise, sadness, etc.). The results are sent to the server.

[0219] Step 5: Adjust task priorities

[0220] The server dynamically adjusts task priorities based on the received emotional data: for example, if the user is feeling stressed, it postpones complex tasks and prioritizes simpler ones.

[0221] Step 6: View the task list

[0222] The terminal displays the task list to the user through a user interface, and the user can check the task list and see information such as the details of each task, the person in charge, the deadline, and the priority.

[0223] Step 7: Update the task

[0224] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0225] Step 8: Remind

[0226] The server monitors the progress of the task list and sends reminders for tasks approaching their deadlines. Based on emotional data, it can send reminders when the user is relaxed, or adjust the timing when the user is under stress. For example, it can send a reminder email or display a pop-up notification one day before the task is due, saying, "Your quotation is incomplete."

[0227] The above are the specific processing steps of the present invention.

[0228] Example 2

[0229] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0230] While conventional email and task management systems are capable of receiving emails and generating tasks based on them, they lack the functionality to dynamically change task priorities while taking into account the user's emotional state. This makes it difficult to appropriately manage users' stress and workload, and has led to issues such as insufficient improvements in work efficiency and reduction of psychological burden.

[0231] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0232] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for displaying and updating the task list through a user interface, means for monitoring the user's emotional state in real time and analyzing emotional data, means for dynamically adjusting task priorities based on the emotional data, and means for adjusting the timing of reminders based on the emotional data. This not only integrates email management and task management, but also dynamically adjusts task priorities and the timing of reminders according to the user's emotional state, thereby improving work efficiency and reducing psychological burden.

[0233] "Means for receiving email" is a function that allows the server to access the email server, obtain new emails, and save them in an analyzable format.

[0234] "Natural language processing technology" is a technology that analyzes grammatical and semantic information from text data and extracts necessary keywords and phrases.

[0235] "Means for classifying into specific categories" refers to a function that automatically sorts the analyzed email content into predefined categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment).

[0236] "Means for automatically generating task lists" is a function that generates new tasks that need to be addressed based on the analysis results and sets the person in charge and deadline.

[0237] The "means for monitoring the progress of a task" is a function for continuously monitoring the progress of a generated task and updating it as necessary.

[0238] The "means for reminding" is a function that notifies the user that a task that should be addressed is incomplete when the deadline for the task approaches.

[0239] A "user interface" is an interactive screen that allows a user to view a task list, update task progress, and create new tasks.

[0240] "Means for monitoring in real time" refers to a function that constantly monitors the user's emotional state and immediately collects data if there is a change.

[0241] The "means for analyzing emotional data" is a technology that analyzes collected emotional data and determines the user's psychological state (e.g., joy, anger, sadness, surprise).

[0242] The "means for dynamically adjusting task priorities" is a function that appropriately changes the importance and urgency of tasks based on the user's emotional data, optimizing scheduling to reduce the user's burden.

[0243] The "means for adjusting the timing of reminders" is a function that adjusts the timing and frequency of reminders taking into account the user's emotional state, and notifies them at the optimal time.

[0244] MODE FOR CARRYING OUT THE INVENTION

[0245] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0246] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored as text files in a specific folder or database on the file system.

[0247] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology can be achieved using libraries such as NLTK or spaCy. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. Categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0248] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list and saved in the database.

[0249] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data. The analysis results are immediately transmitted to the server.

[0250] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. For example, one method is to send reminders when the user is relaxed, and conversely, to raise the threshold when the user is highly stressed.

[0251] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0252] Furthermore, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it can send a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0253] Specific examples

[0254] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0255] Prompt Sentence Examples

[0256] "A new request for quote has arrived. Please analyze the contents of this email and add it to your task list."

[0257] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0258] Step 1:

[0259] The server periodically accesses the mail server to check for new emails. Specifically, it connects to the mail server using the IMAP or POP3 protocol and retrieves a list of new emails. The input is the connection information for the mail server, and the output is a list of new emails.

[0260] Step 2:

[0261] The server retrieves new emails and converts them to text format. This involves converting the email body to plain text format and extracting attachment information if any. The input is a list of new emails, and the output is email data in text format. Specifically, each email is saved as a text file.

[0262] Step 3:

[0263] The server reads the textual email data and parses it using natural language processing (NLP) techniques. It uses an NLP library (e.g., NLTK or spaCy) to extract important keywords and phrases from the email body. The input is the textual email data, and the output is the extracted keywords and phrases.

[0264] Step 4:

[0265] The server classifies the email content into specific categories based on the extracted keywords and phrases. Categories include "quote request," "inquiry," "order procedure," and "schedule adjustment." The input is the extracted keywords and phrases, and the output is category information. Specifically, the category is determined by checking against a predefined keyword list.

[0266] Step 5:

[0267] The server automatically generates a task list based on the classified category. An appropriate person in charge and a deadline are set for each task. The input is category information, and the output is a task list. For example, an email classified as "Request for quotation" generates a task called "Create a quotation," with a sales representative set as the person in charge and a deadline of three days after receipt.

[0268] Step 6:

[0269] The server saves the generated task list in a database. The input is the task list, and the output is the task information saved in the database. Specifically, the task list is inserted into an SQL database.

[0270] Step 7:

[0271] The device monitors the user's emotional state in real time and sends the collected data to the emotion engine. The input is camera footage and audio data, and the output is emotion data. Specifically, emotions are determined using facial recognition and voice analysis technology.

[0272] Step 8:

[0273] The server receives emotion data from the emotion engine and dynamically adjusts task priorities. The input is emotion data, and the output is a re-adjusted task list. For example, if a user feels very stressed, some tasks are postponed.

[0274] Step 9:

[0275] The server adjusts the timing of reminders based on the emotional data. The input is emotional data, and the output is the timing of reminders. For example, reminders can be sent when the user is relaxed, and can be withheld when the user is under stress.

[0276] Step 10:

[0277] The user terminal displays the generated task list through a user interface. The user checks the task list and updates the progress of each task through the interface. The input is the task list, and the output is the updated task list. For example, when the "Create a quote" task is completed, the task is marked as "Completed."

[0278] Step 11:

[0279] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is a task list, and the output is a reminder notification. For example, a reminder email saying "The quotation creation is incomplete" is sent to the user one day before the task deadline.

[0280] (Application example 2)

[0281] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0282] While conventional email analysis and task management systems effectively analyze received emails and generate tasks, they do not prioritize tasks or adjust them in real time based on the user's emotional state. This can lead to stress for specific users and imbalances in work loads, resulting in a loss of work efficiency and balance. Furthermore, in work environments such as factories, it is difficult to dynamically adjust tasks, resulting in insufficient optimization of collaboration between robots and humans. Therefore, a system that can grasp a user's emotional state in real time and assign tasks and send reminders at the appropriate time is needed.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0284] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for recognizing the user's emotional state in real time, means for dynamically changing task priorities based on the user's emotional state, and means for displaying and updating the task list through a user interface. This allows the automatic adjustment of task priorities according to the user's emotional state, thereby enabling work efficiency and optimal balance.

[0285] "Means for receiving email" refers to a function that allows a server or terminal to automatically obtain new email from a mail server via a network such as the Internet.

[0286] "A means of analyzing received emails using natural language processing technology and classifying the email contents into specific categories" refers to a function that uses natural language processing (NLP) technology to extract important keywords and phrases from the email body, and then automatically classifies the email contents into specific categories, such as "request for quotation" or "inquiry," based on that.

[0287] "Means for automatically generating a task list based on the analyzed email content" is a function that automatically generates necessary tasks based on the analysis results and sets appropriate people in charge and deadlines.

[0288] The "means for monitoring task progress and sending reminders for tasks approaching their deadlines" is a function that constantly monitors the progress of the generated task list and sends reminders to the user when a task's deadline approaches.

[0289] "Means for recognizing the user's emotional state in real time" refers to a function that recognizes and grasps the user's emotional state (joy, anger, sadness, surprise, etc.) in real time by analyzing camera footage and audio data.

[0290] "Means for dynamically changing task priorities based on the user's emotional state" is a function that dynamically adjusts the priority and schedule of tasks in a task list based on the user's emotional data recognized in real time.

[0291] "Means for displaying and updating task lists through a user interface" is a function that displays the generated task list in an easy-to-understand manner to the user and provides an interface that allows the user to update the progress and completion status of tasks at any time.

[0292] This invention is a system for improving the efficiency of work management and task coordination in factories. This system automatically receives and analyzes emails, generates and manages tasks based on the content of the emails, and has the function of dynamically changing task priorities by recognizing the user's emotional state in real time.

[0293] First, the server receives emails from the factory manager. The server periodically accesses the mail server to retrieve new emails and converts them into text format. This email text is then saved in a file or database in an easy-to-parse format.

[0294] The server then analyzes the text of the saved email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body and automatically classifies the content into specific categories such as "quote request" or "inquiry." Based on this classification information, the server automatically generates a task list. An appropriate robot and deadline are assigned to each task. For example, if the email says "Please align the parts," a task called "align the parts" is generated, and the assigned robot is set to Robot A in the factory.

[0295] One of its distinctive features is that it incorporates an emotion engine that recognizes the user's emotions. The device analyzes camera footage and audio data to recognize the user's emotional state, such as joy, anger, sadness, or surprise, in real time. The recognized user emotion data is sent to the server and used to dynamically change task priorities. For example, if a line manager is feeling stressed, adjustments can be made, such as extending the deadline for a task. Also, if the user is feeling relaxed, measures can be taken, such as increasing the frequency of reminders.

[0296] The generated task list is displayed through a user interface. The user can check the progress of the tasks and update the progress of each task through the interface. For example, when a user completes the "align parts" task, the user can mark the task as "completed." The server monitors the progress of the tasks and sends reminders for tasks whose deadlines are approaching. Reminders are automatically sent before the task deadline, helping users to complete tasks without forgetting.

[0297] For illustrative purposes, consider the following prompt:

[0298] Analyze an email with the content "Please assign robot A to align the parts," generate appropriate tasks, and adjust the task priority taking into account the user's emotional state (stress, relaxation, etc.).

[0299] The hardware required to implement this system includes a server, mail server, client terminals (PCs and tablets), cameras, and microphones. The software includes Python, TextBlob, a mail analysis library, and an emotion recognition engine. This streamlines task management within the factory and enables flexible responses based on the user's emotional state.

[0300] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0301] Step 1:

[0302] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database. The input is new emails and the output is text format email data. This process involves retrieving emails and converting them into text format.

[0303] Step 2:

[0304] The server analyzes the text of the stored emails using natural language processing (NLP) techniques. The input is the email data in text format, and the output is extracted keywords and phrases. Specifically, it uses an NLP library such as TextBlob to extract important keywords and classify the content into specific categories.

[0305] Step 3:

[0306] The server automatically generates a task list based on the analysis results. The input is the keywords and categories resulting from the analysis, and the output is the task list. An appropriate robot and deadline are set for each task. Specifically, tasks such as "aligning parts" and "checking equipment" are defined based on the category, and a robot and deadline are assigned to each.

[0307] Step 4:

[0308] The device recognizes the user's emotional state in real time. The input is the user's camera video and audio data, and the output is the recognized emotional data. Using an emotion engine, the device analyzes the user's emotions, such as joy, anger, sadness, and surprise, and captures them as data.

[0309] Step 5:

[0310] The server dynamically adjusts task priorities based on the user's emotional data. The input is the emotional data and the task list, and the output is the adjusted task list. If the user is stressed, the deadline is extended, and if the user is relaxed, the frequency of reminders is increased.

[0311] Step 6:

[0312] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is the task list and the current time, and the output is a reminder notification. Specifically, a reminder email such as "The part alignment task is incomplete" is automatically sent one day before the deadline.

[0313] Step 7:

[0314] Users view and update their task lists through a user interface. The input is the task list and user actions, and the output is the updated task list. Using this interface, users can check the progress of tasks and mark completed tasks as "completed."

[0315] By following the above steps, it is possible to carry out everything from email analysis to task management, and even dynamic adjustment of tasks according to the user's emotions.

[0316] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0317] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0318] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0319] [Second embodiment]

[0320] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0321] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0322] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0323] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0324] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0325] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0326] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0327] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0328] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0329] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0330] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0331] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0332] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0333] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database.

[0334] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body, and classifies the email content into specific categories. Examples of categories used in this classification include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0335] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate person and deadline. For example, in the case of a "quote request" email, a task called "Create a quote" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt.

[0336] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0337] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0338] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0339] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are ensured to be completed without being overlooked. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0340] The above is an embodiment of the present invention.

[0341] The processing flow will be explained below.

[0342] Step 1: Receiving email

[0343] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0344] Step 2: Analyzing the email

[0345] The server analyzes the text of the stored emails using natural language processing technology. It extracts important keywords and phrases from the email body and classifies the content into specific categories. In this process, categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment" are used.

[0346] Step 3: Creating tasks

[0347] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote," sets the person in charge to a sales representative, and sets the deadline to three days after receipt. The generated task is added to the task list.

[0348] Step 4: View the task list

[0349] The terminal displays the task list to the user. The user can check the task list through the user interface. Details of each task, the person in charge, the deadline, and other information are displayed.

[0350] Step 5: Update the task

[0351] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0352] Step 6: Remind

[0353] The server monitors the progress of the task list and sends reminders for tasks that are approaching their deadline. For example, a reminder email is sent to the user one day before the task deadline, saying, "The quotation creation is incomplete."

[0354] The above are the specific processing steps of the program of the present invention.

[0355] Example 1

[0356] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0357] Traditional email and task management systems required users to manually analyze email content and create and manage tasks, which was inefficient. Manually monitoring task progress and managing deadlines was also cumbersome, potentially resulting in work delays. Furthermore, there was a lack of consistency and accuracy in classifying tasks into specific categories, leading to the risk of important tasks being overlooked.

[0358] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0359] In this invention, the server includes means for receiving emails, means for analyzing received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for assigning appropriate personnel and deadlines to each task, means for monitoring the progress of tasks and sending reminders for tasks approaching deadlines, means for displaying and updating the task list through a user interface, and means for using a generative AI model in natural language processing technology. This enables automatic analysis of email content, highly accurate and consistent categorization, and task generation and management. Furthermore, the reminder function ensures thorough deadline management and prevents work delays.

[0360] "Means for receiving email" refers to a device or software that has the function of periodically receiving new email from a mail server.

[0361] "Natural language processing technology" refers to methods and technologies for analyzing natural language and extracting meaning from text data, and examples include NLP libraries and models.

[0362] A "means for classifying into specific categories" is a device or software that has the function of analyzing the content of received emails and classifying them into preset categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment."

[0363] The "means for automatically generating a task list" refers to a device or software that has the function of listing the necessary tasks based on the analyzed email content, and allowing the system to automatically create the task list.

[0364] The "means for setting an appropriate person in charge and a deadline" is a device or software that has the function of assigning an appropriate person in charge to a generated task and setting a corresponding deadline.

[0365] The "means for monitoring the progress of a task" refers to a device or software that has the function of continuously monitoring the progress of a generated task and grasping the current status.

[0366] The "means for sending reminders" refers to a device or software that has the function of sending reminder notifications to the person in charge when the deadline for a task approaches.

[0367] The "means for displaying and updating a task list through a user interface" refers to a device or software that displays a task list on a user terminal and allows the user to change or update the progress status of tasks through the interface.

[0368] A "generative AI model" is an artificial intelligence model that realizes various data analysis and generation functions, including natural language processing, and specific examples include models such as GPT and BERT.

[0369] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0370] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze. This conversion and saving process uses, for example, Python's IMAP library, and saves the received emails in a local text file or database.

[0371] The server then analyzes the text of the stored emails using natural language processing (NLP) techniques, such as the NLTK library or spaCy. These tools extract important keywords and phrases from the email body, and based on the results, classify the emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment." Generative AI models (such as GPT and BERT) are also applied to improve the accuracy of the analysis.

[0372] Based on the analysis results, the server automatically generates a task list. This task list is generated according to pre-set rules. For example, in the case of a "quote request" email, a task called "Create a quote" is automatically generated, assigned to a sales representative, and the due date is set to three days after receipt.

[0373] The generated task list is displayed on the user's device through a user interface. The user interface can be a front-end application built using React or Vue.js, for example. Through this interface, the user can check the task list and update the progress of each task. For example, when a user completes the "Create a quote" task, they can mark the task as "Complete."

[0374] The server also monitors the progress of tasks and sends reminders for tasks that are approaching their deadlines. For example, an email sending script is used to send a reminder email to the user one day before the task is due, saying "Your quotation is not yet complete."

[0375] Examples and prompts

[0376] Specific examples

[0377] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0378] Prompt Sentence Examples

[0379] "Receive a new email and classify it into a category called 'Request for quote' based on its content. Next, create a task called 'Create quote' and set the assignee and deadline. Then, display the created task in the user interface."

[0380] This invention integrates email management and task management, automatically analyzes email content, and converts it into appropriate tasks, ensuring that important tasks are not overlooked and are carried out reliably. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0381] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0382] Step 1:

[0383] The server accesses the mail server to check for new mail and receives it.

[0384] Input: Request to access mail server

[0385] Data processing: Retrieve new emails from the mail server using the IMAP or POP3 protocol.

[0386] Output: Retrieved email data

[0387] Specific operation: The server uses Python's IMAP library to connect to the mail server and retrieve new emails. The retrieved emails are temporarily stored in memory.

[0388] Step 2:

[0389] The server converts the received email into text format and stores it in a format that is easy to analyze.

[0390] Input: Received email data

[0391] Data processing: Analyzes the HTML and MIME structure of the email and extracts the text portion.

[0392] Output: Email data converted to text format

[0393] What it does: The server extracts the text of the email and saves it to a local text file or database, along with metadata such as the sender and the date and time the email was received.

[0394] Step 3:

[0395] The server analyzes the text of the stored emails using natural language processing (NLP) techniques.

[0396] Input: Email data in text format

[0397] Data processing: Using inductive analysis techniques, important keywords and phrases are extracted.

[0398] Output: Extracted keywords and phrases

[0399] What it does: The server uses the NLTK library and spaCy to parse the text and extract key keywords and phrases. Optionally, it also leverages generative AI models (e.g., GPT and BERT) to improve the accuracy of the analysis.

[0400] Step 4:

[0401] The server classifies emails into specific categories based on the extracted keywords and phrases.

[0402] Input: Extracted keywords and phrases

[0403] Data crunching: Applying classification algorithms using keyword matching and machine learning models.

[0404] Output: Categorized email data

[0405] How it works: The server uses SciKit-Learn's clustering algorithm to classify emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment."

[0406] Step 5:

[0407] The server automatically generates a task list based on the analysis results.

[0408] Input: Categorized email data

[0409] Data processing: Convert into task list format and set the necessary information (task name, person in charge, deadline, etc.).

[0410] Output: Generated task list

[0411] Specific operation: The server generates tasks according to the category of the email based on pre-defined rules. For example, if an email is a "quote request," a task called "Create a quote" is automatically generated, and a person in charge and deadline are set.

[0412] Step 6:

[0413] The server transmits the generated task list to the user terminal.

[0414] Input: Generated task list

[0415] Data processing: Convert the task list into an interface format.

[0416] Output: Task list data sent to the user's terminal

[0417] Specific operation: The server acts as the backend system of the web application, retrieving the generated task list from the database and sending it to the frontend.

[0418] Step 7:

[0419] The user terminal displays the task list through a user interface.

[0420] Input: Task list data sent from the server

[0421] Data processing: Convert the task list into a display format

[0422] Output: Task list displayed on the user interface

[0423] Specific operation: The user device displays a task list in dashboard format on a front end built using React and Vue.js.

[0424] Step 8:

[0425] The user can view the task list and update the progress of each task through the interface.

[0426] Input: Task list on the user interface

[0427] Data manipulation: Updating task progress

[0428] Output: Updated task list

[0429] How it works: Users can change the status of each task to "in progress" or "completed" in the interface, and that information is updated in the database in real time.

[0430] Step 9:

[0431] The server monitors the progress of tasks and provides reminders for tasks approaching deadlines.

[0432] Input: Task list and progress information in a database

[0433] Data calculation: Check deadlines and extract tasks that need reminders

[0434] Output: Send reminder email

[0435] What it does: The server uses a periodic job (e.g. a Cron job) to check the task progress and deadline information from the database, and generates and sends reminder emails to users.

[0436] (Application example 1)

[0437] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0438] In logistics centers, tasks requested via email (e.g., shipping preparation, inventory checks, inspection work, etc.) are often managed manually. This results in tasks being overlooked or delayed, leading to problems with reduced work efficiency. Furthermore, there is a lack of real-time feedback that allows employees to work efficiently. To resolve these operational inefficiencies, a system is needed that can automatically analyze the content of emails and generate and manage tasks based on that analysis.

[0439] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0440] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for monitoring the progress of tasks and sending reminders for tasks with approaching deadlines, means for displaying and updating the task list through a user interface, and means for displaying the task list on a smart device and providing visual feedback on the update status in real time. This enables automation of task management in logistics centers, preventing task oversights and delays and significantly improving work efficiency. By allowing employees to work while checking the progress of tasks in real time on their smart devices, work efficiency is improved and errors are reduced.

[0441] "Means for receiving email" refers to a device or system that has the function of periodically receiving new emails from an email server.

[0442] "Means for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories" refers to a device or system that analyzes the text data of received emails, analyzes the content using natural language processing technology, and classifies the content into appropriate categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment, shipping preparation, inventory check, inspection work, etc.).

[0443] The "means for automatically generating a task list based on the analyzed email content" refers to a device or system that has the function of automatically generating corresponding tasks in list form based on the results of email analysis.

[0444] A "means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching" is a device or system that has the function of monitoring the progress of each task included in a task list in real time and sending reminder notifications when the deadlines are approaching.

[0445] A "means for displaying and updating a task list through a user interface" is a device or system that provides an interface that allows a user to visually view a task list and update the status of each task.

[0446] "Means for displaying a task list on a smart device and providing real-time visual feedback on the update status" refers to a device or system that has the function of displaying a task list on smart glasses or other portable digital devices and providing real-time visual feedback on the progress of tasks.

[0447] The present invention is a system designed for efficient task management in a logistics center. This system has the function of automatically analyzing received emails and generating and managing a task list based on the analysis. The following describes in detail an embodiment of the present invention.

[0448] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database. The server then analyzes the saved email text using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body. As a result, the email content is classified into specific categories. Categories used in this classification process include "quote request," "inquiry," "order procedure," "schedule adjustment," "shipment preparation," "inventory check," and "inspection work."

[0449] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate assignee and deadline. For example, for a "Prepare for shipment" email, a task called "Prepare for shipment" is generated, with the assignee set to logistics staff and the deadline set to three days after receipt. The generated task list is displayed through a user interface on the smart device (e.g., smart glasses). The user can check the task list and update the progress of each task through the interface. For example, when a user completes the "Prepare for shipment" task, they can mark the task as "Completed."

[0450] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, a reminder notification stating "Shipping preparation work is not yet complete" can be sent to smart devices one day before the task deadline. Employees can work while checking the progress of their tasks in real time, improving work efficiency and reducing errors.

[0451] This system is implemented using the following hardware and software. The hardware includes smart glasses and a server. The software includes Python, imaplib, email, sklearn, and spacy. The server uses this software to receive emails, analyze text, generate tasks, and send reminders. The smart glasses display a task list in real time and accept input from the user.

[0452] As a concrete example, if a logistics center manager receives an email saying, "Please prepare for shipment," the system automatically analyzes the email's contents, generates a task called "prepare for shipment," and displays it on the smart glasses. Employees can then confirm the task, prepare for shipment, and report completion to the system through the smart glasses. This automates task management and enables efficient business operations.

[0453] Example prompt sentence:

[0454] "Please prepare shipment for new orders."

[0455] "Please check stock availability"

[0456] "Please inspect the new shipping list."

[0457] The above is an embodiment of the present invention.

[0458] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0459] Step 1:

[0460] The server periodically accesses the mail server and receives new mail.

[0461] Specifically, the server connects to the mail server using the IMAP protocol and searches for "unread mail," resulting in a list of new mail.

[0462] Input: Mail server account information, IMAP protocol

[0463] Output: List of new emails

[0464] Step 2:

[0465] The server converts the received email into text format and saves it in a format that is easy to analyze.

[0466] Specifically, the email body is extracted and saved in a text file or database.

[0467] Input: List of new emails

[0468] Output: Plain text of the email body (saved file or database entry)

[0469] Step 3:

[0470] The server analyzes the text of stored emails using natural language processing (NLP) techniques.

[0471] Specifically, the server uses the spacy library to parse the email body, extract important keywords and phrases, and classify the content into specific categories.

[0472] Input: Email body in plain text format

[0473] Output: Important keywords and phrases, categorization results

[0474] Step 4:

[0475] The server automatically generates a task list based on the parsed email content.

[0476] Specifically, the server generates appropriate tasks based on keywords and categories, and assigns a person in charge and a deadline to each task.

[0477] Input: Category classification results, keywords

[0478] Output: Auto-generated task list (task name, assignee, due date)

[0479] Step 5:

[0480] The server displays the task list on the smart device and provides visual feedback on the update status in real time.

[0481] Specifically, the server sends the task list to a smart device (e.g., smart glasses) so that the user can check it. When the task progress is updated, the information is displayed in real time.

[0482] Input: Task list

[0483] Output: Task list display on smart device

[0484] Step 6:

[0485] The user views the task list and updates the progress of each task through the interface.

[0486] Specifically, the user updates the task completion status through the smart device, and the updated information is sent to the server, which then updates the task list again.

[0487] Input: User task progress update

[0488] Output: Updated task list

[0489] Step 7:

[0490] The server monitors the progress of tasks and reminds you about tasks that are approaching their deadlines.

[0491] Specifically, the server monitors the deadline for each task in the task list, and when the deadline approaches, it generates a reminder notification and sends it to the smart device.

[0492] Input: Due date for each task in the task list

[0493] Output: Remind notification (notification to smart device)

[0494] The above are the specific processing steps of this system.

[0495] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0496] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0497] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored in a text file or database.

[0498] The server then analyzes the text of the stored email using natural language processing (NLP) technology, which extracts important keywords and phrases from the email body and classifies the content into specific categories. Examples of categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0499] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and a deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list.

[0500] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data.

[0501] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. One method is to send reminders when the user is relaxed, or, conversely, to raise the threshold when the user is highly stressed.

[0502] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0503] In addition, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "Your quotation is not yet complete."

[0504] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0505] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are not overlooked and are reliably progressed. Furthermore, by adding a user emotion recognition function using an emotion engine, task priorities are adjusted and reminders are set according to the user's state, thereby reducing workload imbalances and stress.

[0506] The above is an embodiment of the present invention.

[0507] The processing flow will be explained below.

[0508] Step 1: Receiving email

[0509] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0510] Step 2: Analyzing the email

[0511] The server analyzes the text of the stored emails using natural language processing technology. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. In this classification process, categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment" are used.

[0512] Step 3: Creating tasks

[0513] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote." The person in charge is set to a sales representative, and the deadline is set to three days after receipt. The generated task is added to the task list.

[0514] Step 4: Receiving emotion data

[0515] The device collects data from the camera and microphone to monitor the user's emotional state in real time. The emotion engine analyzes this data and recognizes the user's emotional state (happiness, anger, surprise, sadness, etc.). The results are sent to the server.

[0516] Step 5: Adjust task priorities

[0517] The server dynamically adjusts task priorities based on the received emotional data: for example, if the user is feeling stressed, it postpones complex tasks and prioritizes simpler ones.

[0518] Step 6: View the task list

[0519] The terminal displays the task list to the user through a user interface, and the user can check the task list and see information such as the details of each task, the person in charge, the deadline, and the priority.

[0520] Step 7: Update the task

[0521] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0522] Step 8: Remind

[0523] The server monitors the progress of the task list and sends reminders for tasks approaching their deadlines. Based on emotional data, it can send reminders when the user is relaxed, or adjust the timing when the user is under stress. For example, it can send a reminder email or display a pop-up notification one day before the task is due, saying, "Your quotation is incomplete."

[0524] The above are the specific processing steps of the present invention.

[0525] Example 2

[0526] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0527] While conventional email and task management systems are capable of receiving emails and generating tasks based on them, they lack the functionality to dynamically change task priorities while taking into account the user's emotional state. This makes it difficult to appropriately manage users' stress and workload, and has led to issues such as insufficient improvements in work efficiency and reduction of psychological burden.

[0528] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0529] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for displaying and updating the task list through a user interface, means for monitoring the user's emotional state in real time and analyzing emotional data, means for dynamically adjusting task priorities based on the emotional data, and means for adjusting the timing of reminders based on the emotional data. This not only integrates email management and task management, but also dynamically adjusts task priorities and the timing of reminders according to the user's emotional state, thereby improving work efficiency and reducing psychological burden.

[0530] "Means for receiving email" is a function that allows the server to access the email server, obtain new emails, and save them in an analyzable format.

[0531] "Natural language processing technology" is a technology that analyzes grammatical and semantic information from text data and extracts necessary keywords and phrases.

[0532] "Means for classifying into specific categories" refers to a function that automatically sorts the analyzed email content into predefined categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment).

[0533] "Means for automatically generating task lists" is a function that generates new tasks that need to be addressed based on the analysis results and sets the person in charge and deadline.

[0534] The "means for monitoring the progress of a task" is a function for continuously monitoring the progress of a generated task and updating it as necessary.

[0535] The "means for reminding" is a function that notifies the user that a task that should be addressed is incomplete when the deadline for the task approaches.

[0536] A "user interface" is an interactive screen that allows a user to view a task list, update task progress, and create new tasks.

[0537] "Means for monitoring in real time" refers to a function that constantly monitors the user's emotional state and immediately collects data if there is a change.

[0538] The "means for analyzing emotional data" is a technology that analyzes collected emotional data and determines the user's psychological state (e.g., joy, anger, sadness, surprise).

[0539] The "means for dynamically adjusting task priorities" is a function that appropriately changes the importance and urgency of tasks based on the user's emotional data, optimizing scheduling to reduce the user's burden.

[0540] The "means for adjusting the timing of reminders" is a function that adjusts the timing and frequency of reminders taking into account the user's emotional state, and notifies them at the optimal time.

[0541] MODE FOR CARRYING OUT THE INVENTION

[0542] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0543] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored as text files in a specific folder or database on the file system.

[0544] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology can be achieved using libraries such as NLTK or spaCy. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. Categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0545] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list and saved in the database.

[0546] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data. The analysis results are immediately transmitted to the server.

[0547] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. For example, one method is to send reminders when the user is relaxed, and conversely, to raise the threshold when the user is highly stressed.

[0548] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0549] Furthermore, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it can send a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0550] Specific examples

[0551] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0552] Prompt Sentence Examples

[0553] "A new request for quote has arrived. Please analyze the contents of this email and add it to your task list."

[0554] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0555] Step 1:

[0556] The server periodically accesses the mail server to check for new emails. Specifically, it connects to the mail server using the IMAP or POP3 protocol and retrieves a list of new emails. The input is the connection information for the mail server, and the output is a list of new emails.

[0557] Step 2:

[0558] The server retrieves new emails and converts them to text format. This involves converting the email body to plain text format and extracting attachment information if any. The input is a list of new emails, and the output is email data in text format. Specifically, each email is saved as a text file.

[0559] Step 3:

[0560] The server reads the textual email data and parses it using natural language processing (NLP) techniques. It uses an NLP library (e.g., NLTK or spaCy) to extract important keywords and phrases from the email body. The input is the textual email data, and the output is the extracted keywords and phrases.

[0561] Step 4:

[0562] The server classifies the email content into specific categories based on the extracted keywords and phrases. Categories include "quote request," "inquiry," "order procedure," and "schedule adjustment." The input is the extracted keywords and phrases, and the output is category information. Specifically, the category is determined by checking against a predefined keyword list.

[0563] Step 5:

[0564] The server automatically generates a task list based on the classified category. An appropriate person in charge and a deadline are set for each task. The input is category information, and the output is a task list. For example, an email classified as "Request for quotation" generates a task called "Create a quotation," with a sales representative set as the person in charge and a deadline of three days after receipt.

[0565] Step 6:

[0566] The server saves the generated task list in a database. The input is the task list, and the output is the task information saved in the database. Specifically, the task list is inserted into an SQL database.

[0567] Step 7:

[0568] The device monitors the user's emotional state in real time and sends the collected data to the emotion engine. The input is camera footage and audio data, and the output is emotion data. Specifically, emotions are determined using facial recognition and voice analysis technology.

[0569] Step 8:

[0570] The server receives emotion data from the emotion engine and dynamically adjusts task priorities. The input is emotion data, and the output is a re-adjusted task list. For example, if a user feels very stressed, some tasks are postponed.

[0571] Step 9:

[0572] The server adjusts the timing of reminders based on the emotional data. The input is emotional data, and the output is the timing of reminders. For example, reminders can be sent when the user is relaxed, and can be withheld when the user is under stress.

[0573] Step 10:

[0574] The user terminal displays the generated task list through a user interface. The user checks the task list and updates the progress of each task through the interface. The input is the task list, and the output is the updated task list. For example, when the "Create a quote" task is completed, the task is marked as "Completed."

[0575] Step 11:

[0576] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is a task list, and the output is a reminder notification. For example, a reminder email saying "The quotation creation is incomplete" is sent to the user one day before the task deadline.

[0577] (Application example 2)

[0578] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] While conventional email analysis and task management systems effectively analyze received emails and generate tasks, they do not prioritize tasks or adjust them in real time based on the user's emotional state. This can lead to stress for specific users and imbalances in work loads, resulting in a loss of work efficiency and balance. Furthermore, in work environments such as factories, it is difficult to dynamically adjust tasks, resulting in insufficient optimization of collaboration between robots and humans. Therefore, a system that can grasp a user's emotional state in real time and assign tasks and send reminders at the appropriate time is needed.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0581] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for recognizing the user's emotional state in real time, means for dynamically changing task priorities based on the user's emotional state, and means for displaying and updating the task list through a user interface. This allows the automatic adjustment of task priorities according to the user's emotional state, thereby enabling work efficiency and optimal balance.

[0582] "Means for receiving email" refers to a function that allows a server or terminal to automatically obtain new email from a mail server via a network such as the Internet.

[0583] "A means of analyzing received emails using natural language processing technology and classifying the email contents into specific categories" refers to a function that uses natural language processing (NLP) technology to extract important keywords and phrases from the email body, and then automatically classifies the email contents into specific categories, such as "request for quotation" or "inquiry," based on that.

[0584] "Means for automatically generating a task list based on the analyzed email content" is a function that automatically generates necessary tasks based on the analysis results and sets appropriate people in charge and deadlines.

[0585] The "means for monitoring task progress and sending reminders for tasks approaching their deadlines" is a function that constantly monitors the progress of the generated task list and sends reminders to the user when a task's deadline approaches.

[0586] "Means for recognizing the user's emotional state in real time" refers to a function that recognizes and grasps the user's emotional state (joy, anger, sadness, surprise, etc.) in real time by analyzing camera footage and audio data.

[0587] "Means for dynamically changing task priorities based on the user's emotional state" is a function that dynamically adjusts the priority and schedule of tasks in a task list based on the user's emotional data recognized in real time.

[0588] "Means for displaying and updating task lists through a user interface" is a function that displays the generated task list in an easy-to-understand manner to the user and provides an interface that allows the user to update the progress and completion status of tasks at any time.

[0589] This invention is a system for improving the efficiency of work management and task coordination in factories. This system automatically receives and analyzes emails, generates and manages tasks based on the content of the emails, and has the function of dynamically changing task priorities by recognizing the user's emotional state in real time.

[0590] First, the server receives emails from the factory manager. The server periodically accesses the mail server to retrieve new emails and converts them into text format. This email text is then saved in a file or database in an easy-to-parse format.

[0591] The server then analyzes the text of the saved email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body and automatically classifies the content into specific categories such as "quote request" or "inquiry." Based on this classification information, the server automatically generates a task list. An appropriate robot and deadline are assigned to each task. For example, if the email says "Please align the parts," a task called "align the parts" is generated, and the assigned robot is set to Robot A in the factory.

[0592] One of its distinctive features is that it incorporates an emotion engine that recognizes the user's emotions. The device analyzes camera footage and audio data to recognize the user's emotional state, such as joy, anger, sadness, or surprise, in real time. The recognized user emotion data is sent to the server and used to dynamically change task priorities. For example, if a line manager is feeling stressed, adjustments can be made, such as extending the deadline for a task. Also, if the user is feeling relaxed, measures can be taken, such as increasing the frequency of reminders.

[0593] The generated task list is displayed through a user interface. The user can check the progress of the tasks and update the progress of each task through the interface. For example, when a user completes the "align parts" task, the user can mark the task as "completed." The server monitors the progress of the tasks and sends reminders for tasks whose deadlines are approaching. Reminders are automatically sent before the task deadline, helping users to complete tasks without forgetting.

[0594] For illustrative purposes, consider the following prompt:

[0595] Analyze an email with the content "Please assign robot A to align the parts," generate appropriate tasks, and adjust the task priority taking into account the user's emotional state (stress, relaxation, etc.).

[0596] The hardware required to implement this system includes a server, mail server, client terminals (PCs and tablets), cameras, and microphones. The software includes Python, TextBlob, a mail analysis library, and an emotion recognition engine. This streamlines task management within the factory and enables flexible responses based on the user's emotional state.

[0597] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0598] Step 1:

[0599] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database. The input is new emails and the output is text format email data. This process involves retrieving emails and converting them into text format.

[0600] Step 2:

[0601] The server analyzes the text of the stored emails using natural language processing (NLP) techniques. The input is the email data in text format, and the output is extracted keywords and phrases. Specifically, it uses an NLP library such as TextBlob to extract important keywords and classify the content into specific categories.

[0602] Step 3:

[0603] The server automatically generates a task list based on the analysis results. The input is the keywords and categories resulting from the analysis, and the output is the task list. An appropriate robot and deadline are set for each task. Specifically, tasks such as "aligning parts" and "checking equipment" are defined based on the category, and a robot and deadline are assigned to each.

[0604] Step 4:

[0605] The device recognizes the user's emotional state in real time. The input is the user's camera video and audio data, and the output is the recognized emotional data. Using an emotion engine, the device analyzes the user's emotions, such as joy, anger, sadness, and surprise, and captures them as data.

[0606] Step 5:

[0607] The server dynamically adjusts task priorities based on the user's emotional data. The input is the emotional data and the task list, and the output is the adjusted task list. If the user is stressed, the deadline is extended, and if the user is relaxed, the frequency of reminders is increased.

[0608] Step 6:

[0609] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is the task list and the current time, and the output is a reminder notification. Specifically, a reminder email such as "The part alignment task is incomplete" is automatically sent one day before the deadline.

[0610] Step 7:

[0611] Users view and update their task lists through a user interface. The input is the task list and user actions, and the output is the updated task list. Using this interface, users can check the progress of tasks and mark completed tasks as "completed."

[0612] By following the above steps, it is possible to carry out everything from email analysis to task management, and even dynamic adjustment of tasks according to the user's emotions.

[0613] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0614] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0616] [Third embodiment]

[0617] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0618] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0619] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0621] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0623] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0624] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0625] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0626] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0627] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0628] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0629] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0630] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database.

[0631] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body, and classifies the email content into specific categories. Examples of categories used in this classification include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0632] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate person and deadline. For example, in the case of a "quote request" email, a task called "Create a quote" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt.

[0633] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0634] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0635] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0636] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are ensured to be completed without being overlooked. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0637] The above is an embodiment of the present invention.

[0638] The processing flow will be explained below.

[0639] Step 1: Receiving email

[0640] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0641] Step 2: Analyzing the email

[0642] The server analyzes the text of the stored emails using natural language processing technology. It extracts important keywords and phrases from the email body and classifies the content into specific categories. In this process, categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment" are used.

[0643] Step 3: Creating tasks

[0644] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote," sets the person in charge to a sales representative, and sets the deadline to three days after receipt. The generated task is added to the task list.

[0645] Step 4: View the task list

[0646] The terminal displays the task list to the user. The user can check the task list through the user interface. Details of each task, the person in charge, the deadline, and other information are displayed.

[0647] Step 5: Update the task

[0648] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0649] Step 6: Remind

[0650] The server monitors the progress of the task list and sends reminders for tasks that are approaching their deadline. For example, a reminder email is sent to the user one day before the task deadline, saying, "The quotation creation is incomplete."

[0651] The above are the specific processing steps of the program of the present invention.

[0652] Example 1

[0653] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0654] Traditional email and task management systems required users to manually analyze email content and create and manage tasks, which was inefficient. Manually monitoring task progress and managing deadlines was also cumbersome, potentially resulting in work delays. Furthermore, there was a lack of consistency and accuracy in classifying tasks into specific categories, leading to the risk of important tasks being overlooked.

[0655] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0656] In this invention, the server includes means for receiving emails, means for analyzing received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for assigning appropriate personnel and deadlines to each task, means for monitoring the progress of tasks and sending reminders for tasks approaching deadlines, means for displaying and updating the task list through a user interface, and means for using a generative AI model in natural language processing technology. This enables automatic analysis of email content, highly accurate and consistent categorization, and task generation and management. Furthermore, the reminder function ensures thorough deadline management and prevents work delays.

[0657] "Means for receiving email" refers to a device or software that has the function of periodically receiving new email from a mail server.

[0658] "Natural language processing technology" refers to methods and technologies for analyzing natural language and extracting meaning from text data, and examples include NLP libraries and models.

[0659] A "means for classifying into specific categories" is a device or software that has the function of analyzing the content of received emails and classifying them into preset categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment."

[0660] The "means for automatically generating a task list" refers to a device or software that has the function of listing the necessary tasks based on the analyzed email content, and allowing the system to automatically create the task list.

[0661] The "means for setting an appropriate person in charge and a deadline" is a device or software that has the function of assigning an appropriate person in charge to a generated task and setting a corresponding deadline.

[0662] The "means for monitoring the progress of a task" refers to a device or software that has the function of continuously monitoring the progress of a generated task and grasping the current status.

[0663] The "means for sending reminders" refers to a device or software that has the function of sending reminder notifications to the person in charge when the deadline for a task approaches.

[0664] The "means for displaying and updating a task list through a user interface" refers to a device or software that displays a task list on a user terminal and allows the user to change or update the progress status of tasks through the interface.

[0665] A "generative AI model" is an artificial intelligence model that realizes various data analysis and generation functions, including natural language processing, and specific examples include models such as GPT and BERT.

[0666] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0667] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze. This conversion and saving process uses, for example, Python's IMAP library, and saves the received emails in a local text file or database.

[0668] The server then analyzes the text of the stored emails using natural language processing (NLP) techniques, such as the NLTK library or spaCy. These tools extract important keywords and phrases from the email body, and based on the results, classify the emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment." Generative AI models (such as GPT and BERT) are also applied to improve the accuracy of the analysis.

[0669] Based on the analysis results, the server automatically generates a task list. This task list is generated according to pre-set rules. For example, in the case of a "quote request" email, a task called "Create a quote" is automatically generated, assigned to a sales representative, and the due date is set to three days after receipt.

[0670] The generated task list is displayed on the user's device through a user interface. The user interface can be a front-end application built using React or Vue.js, for example. Through this interface, the user can check the task list and update the progress of each task. For example, when a user completes the "Create a quote" task, they can mark the task as "Complete."

[0671] The server also monitors the progress of tasks and sends reminders for tasks that are approaching their deadlines. For example, an email sending script is used to send a reminder email to the user one day before the task is due, saying "Your quotation is not yet complete."

[0672] Examples and prompts

[0673] Specific examples

[0674] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0675] Prompt Sentence Examples

[0676] "Receive a new email and classify it into a category called 'Request for quote' based on its content. Next, create a task called 'Create quote' and set the assignee and deadline. Then, display the created task in the user interface."

[0677] This invention integrates email management and task management, automatically analyzes email content, and converts it into appropriate tasks, ensuring that important tasks are not overlooked and are carried out reliably. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0678] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0679] Step 1:

[0680] The server accesses the mail server to check for new mail and receives it.

[0681] Input: Request to access mail server

[0682] Data processing: Retrieve new emails from the mail server using the IMAP or POP3 protocol.

[0683] Output: Retrieved email data

[0684] Specific operation: The server uses Python's IMAP library to connect to the mail server and retrieve new emails. The retrieved emails are temporarily stored in memory.

[0685] Step 2:

[0686] The server converts the received email into text format and stores it in a format that is easy to analyze.

[0687] Input: Received email data

[0688] Data processing: Analyzes the HTML and MIME structure of the email and extracts the text portion.

[0689] Output: Email data converted to text format

[0690] What it does: The server extracts the text of the email and saves it to a local text file or database, along with metadata such as the sender and the date and time the email was received.

[0691] Step 3:

[0692] The server analyzes the text of the stored emails using natural language processing (NLP) techniques.

[0693] Input: Email data in text format

[0694] Data processing: Using inductive analysis techniques, important keywords and phrases are extracted.

[0695] Output: Extracted keywords and phrases

[0696] What it does: The server uses the NLTK library and spaCy to parse the text and extract key keywords and phrases. Optionally, it also leverages generative AI models (e.g., GPT and BERT) to improve the accuracy of the analysis.

[0697] Step 4:

[0698] The server classifies emails into specific categories based on the extracted keywords and phrases.

[0699] Input: Extracted keywords and phrases

[0700] Data crunching: Applying classification algorithms using keyword matching and machine learning models.

[0701] Output: Categorized email data

[0702] How it works: The server uses SciKit-Learn's clustering algorithm to classify emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment."

[0703] Step 5:

[0704] The server automatically generates a task list based on the analysis results.

[0705] Input: Categorized email data

[0706] Data processing: Convert into task list format and set the necessary information (task name, person in charge, deadline, etc.).

[0707] Output: Generated task list

[0708] Specific operation: The server generates tasks according to the category of the email based on pre-defined rules. For example, if an email is a "quote request," a task called "Create a quote" is automatically generated, and a person in charge and deadline are set.

[0709] Step 6:

[0710] The server transmits the generated task list to the user terminal.

[0711] Input: Generated task list

[0712] Data processing: Convert the task list into an interface format.

[0713] Output: Task list data sent to the user's terminal

[0714] Specific operation: The server acts as the backend system of the web application, retrieving the generated task list from the database and sending it to the frontend.

[0715] Step 7:

[0716] The user terminal displays the task list through a user interface.

[0717] Input: Task list data sent from the server

[0718] Data processing: Convert the task list into a display format

[0719] Output: Task list displayed on the user interface

[0720] Specific operation: The user device displays a task list in dashboard format on a front end built using React and Vue.js.

[0721] Step 8:

[0722] The user can view the task list and update the progress of each task through the interface.

[0723] Input: Task list on the user interface

[0724] Data manipulation: Updating task progress

[0725] Output: Updated task list

[0726] How it works: Users can change the status of each task to "in progress" or "completed" in the interface, and that information is updated in the database in real time.

[0727] Step 9:

[0728] The server monitors the progress of tasks and provides reminders for tasks approaching deadlines.

[0729] Input: Task list and progress information in a database

[0730] Data calculation: Check deadlines and extract tasks that need reminders

[0731] Output: Send reminder email

[0732] What it does: The server uses a periodic job (e.g. a Cron job) to check the task progress and deadline information from the database, and generates and sends reminder emails to users.

[0733] (Application example 1)

[0734] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0735] In logistics centers, tasks requested via email (e.g., shipping preparation, inventory checks, inspection work, etc.) are often managed manually. This results in tasks being overlooked or delayed, leading to problems with reduced work efficiency. Furthermore, there is a lack of real-time feedback that allows employees to work efficiently. To resolve these operational inefficiencies, a system is needed that can automatically analyze the content of emails and generate and manage tasks based on that analysis.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0737] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for monitoring the progress of tasks and sending reminders for tasks with approaching deadlines, means for displaying and updating the task list through a user interface, and means for displaying the task list on a smart device and providing visual feedback on the update status in real time. This enables automation of task management in logistics centers, preventing task oversights and delays and significantly improving work efficiency. By allowing employees to work while checking the progress of tasks in real time on their smart devices, work efficiency is improved and errors are reduced.

[0738] "Means for receiving email" refers to a device or system that has the function of periodically receiving new emails from an email server.

[0739] "Means for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories" refers to a device or system that analyzes the text data of received emails, analyzes the content using natural language processing technology, and classifies the content into appropriate categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment, shipping preparation, inventory check, inspection work, etc.).

[0740] The "means for automatically generating a task list based on the analyzed email content" refers to a device or system that has the function of automatically generating corresponding tasks in list form based on the results of email analysis.

[0741] A "means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching" is a device or system that has the function of monitoring the progress of each task included in a task list in real time and sending reminder notifications when the deadlines are approaching.

[0742] A "means for displaying and updating a task list through a user interface" is a device or system that provides an interface that allows a user to visually view a task list and update the status of each task.

[0743] "Means for displaying a task list on a smart device and providing real-time visual feedback on the update status" refers to a device or system that has the function of displaying a task list on smart glasses or other portable digital devices and providing real-time visual feedback on the progress of tasks.

[0744] The present invention is a system designed for efficient task management in a logistics center. This system has the function of automatically analyzing received emails and generating and managing a task list based on the analysis. The following describes in detail an embodiment of the present invention.

[0745] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database. The server then analyzes the saved email text using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body. As a result, the email content is classified into specific categories. Categories used in this classification process include "quote request," "inquiry," "order procedure," "schedule adjustment," "shipment preparation," "inventory check," and "inspection work."

[0746] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate assignee and deadline. For example, for a "Prepare for shipment" email, a task called "Prepare for shipment" is generated, with the assignee set to logistics staff and the deadline set to three days after receipt. The generated task list is displayed through a user interface on the smart device (e.g., smart glasses). The user can check the task list and update the progress of each task through the interface. For example, when a user completes the "Prepare for shipment" task, they can mark the task as "Completed."

[0747] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, a reminder notification stating "Shipping preparation work is not yet complete" can be sent to smart devices one day before the task deadline. Employees can work while checking the progress of their tasks in real time, improving work efficiency and reducing errors.

[0748] This system is implemented using the following hardware and software. The hardware includes smart glasses and a server. The software includes Python, imaplib, email, sklearn, and spacy. The server uses this software to receive emails, analyze text, generate tasks, and send reminders. The smart glasses display a task list in real time and accept input from the user.

[0749] As a concrete example, if a logistics center manager receives an email saying, "Please prepare for shipment," the system automatically analyzes the email's contents, generates a task called "prepare for shipment," and displays it on the smart glasses. Employees can then confirm the task, prepare for shipment, and report completion to the system through the smart glasses. This automates task management and enables efficient business operations.

[0750] Example prompt sentence:

[0751] "Please prepare shipment for new orders."

[0752] "Please check stock availability"

[0753] "Please inspect the new shipping list."

[0754] The above is an embodiment of the present invention.

[0755] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0756] Step 1:

[0757] The server periodically accesses the mail server and receives new mail.

[0758] Specifically, the server connects to the mail server using the IMAP protocol and searches for "unread mail," resulting in a list of new mail.

[0759] Input: Mail server account information, IMAP protocol

[0760] Output: List of new emails

[0761] Step 2:

[0762] The server converts the received email into text format and saves it in a format that is easy to analyze.

[0763] Specifically, the email body is extracted and saved in a text file or database.

[0764] Input: List of new emails

[0765] Output: Plain text of the email body (saved file or database entry)

[0766] Step 3:

[0767] The server analyzes the text of stored emails using natural language processing (NLP) techniques.

[0768] Specifically, the server uses the spacy library to parse the email body, extract important keywords and phrases, and classify the content into specific categories.

[0769] Input: Email body in plain text format

[0770] Output: Important keywords and phrases, categorization results

[0771] Step 4:

[0772] The server automatically generates a task list based on the parsed email content.

[0773] Specifically, the server generates appropriate tasks based on keywords and categories, and assigns a person in charge and a deadline to each task.

[0774] Input: Category classification results, keywords

[0775] Output: Auto-generated task list (task name, assignee, due date)

[0776] Step 5:

[0777] The server displays the task list on the smart device and provides visual feedback on the update status in real time.

[0778] Specifically, the server sends the task list to a smart device (e.g., smart glasses) so that the user can check it. When the task progress is updated, the information is displayed in real time.

[0779] Input: Task list

[0780] Output: Task list display on smart device

[0781] Step 6:

[0782] The user views the task list and updates the progress of each task through the interface.

[0783] Specifically, the user updates the task completion status through the smart device, and the updated information is sent to the server, which then updates the task list again.

[0784] Input: User task progress update

[0785] Output: Updated task list

[0786] Step 7:

[0787] The server monitors the progress of tasks and reminds you about tasks that are approaching their deadlines.

[0788] Specifically, the server monitors the deadline for each task in the task list, and when the deadline approaches, it generates a reminder notification and sends it to the smart device.

[0789] Input: Due date for each task in the task list

[0790] Output: Remind notification (notification to smart device)

[0791] The above are the specific processing steps of this system.

[0792] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0793] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0794] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored in a text file or database.

[0795] The server then analyzes the text of the stored email using natural language processing (NLP) technology, which extracts important keywords and phrases from the email body and classifies the content into specific categories. Examples of categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0796] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and a deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list.

[0797] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data.

[0798] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. One method is to send reminders when the user is relaxed, or, conversely, to raise the threshold when the user is highly stressed.

[0799] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0800] In addition, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "Your quotation is not yet complete."

[0801] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0802] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are not overlooked and are reliably progressed. Furthermore, by adding a user emotion recognition function using an emotion engine, task priorities are adjusted and reminders are set according to the user's state, thereby reducing workload imbalances and stress.

[0803] The above is an embodiment of the present invention.

[0804] The processing flow will be explained below.

[0805] Step 1: Receiving email

[0806] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0807] Step 2: Analyzing the email

[0808] The server analyzes the text of the stored emails using natural language processing technology. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. In this classification process, categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment" are used.

[0809] Step 3: Creating tasks

[0810] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote." The person in charge is set to a sales representative, and the deadline is set to three days after receipt. The generated task is added to the task list.

[0811] Step 4: Receiving emotion data

[0812] The device collects data from the camera and microphone to monitor the user's emotional state in real time. The emotion engine analyzes this data and recognizes the user's emotional state (happiness, anger, surprise, sadness, etc.). The results are sent to the server.

[0813] Step 5: Adjust task priorities

[0814] The server dynamically adjusts task priorities based on the received emotional data: for example, if the user is feeling stressed, it postpones complex tasks and prioritizes simpler ones.

[0815] Step 6: View the task list

[0816] The terminal displays the task list to the user through a user interface, and the user can check the task list and see information such as the details of each task, the person in charge, the deadline, and the priority.

[0817] Step 7: Update the task

[0818] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0819] Step 8: Remind

[0820] The server monitors the progress of the task list and sends reminders for tasks approaching their deadlines. Based on emotional data, it can send reminders when the user is relaxed, or adjust the timing when the user is under stress. For example, it can send a reminder email or display a pop-up notification one day before the task is due, saying, "Your quotation is incomplete."

[0821] The above are the specific processing steps of the present invention.

[0822] Example 2

[0823] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0824] While conventional email and task management systems are capable of receiving emails and generating tasks based on them, they lack the functionality to dynamically change task priorities while taking into account the user's emotional state. This makes it difficult to appropriately manage users' stress and workload, and has led to issues such as insufficient improvements in work efficiency and reduction of psychological burden.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0826] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for displaying and updating the task list through a user interface, means for monitoring the user's emotional state in real time and analyzing emotional data, means for dynamically adjusting task priorities based on the emotional data, and means for adjusting the timing of reminders based on the emotional data. This not only integrates email management and task management, but also dynamically adjusts task priorities and the timing of reminders according to the user's emotional state, thereby improving work efficiency and reducing psychological burden.

[0827] "Means for receiving email" is a function that allows the server to access the email server, obtain new emails, and save them in an analyzable format.

[0828] "Natural language processing technology" is a technology that analyzes grammatical and semantic information from text data and extracts necessary keywords and phrases.

[0829] "Means for classifying into specific categories" refers to a function that automatically sorts the analyzed email content into predefined categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment).

[0830] "Means for automatically generating task lists" is a function that generates new tasks that need to be addressed based on the analysis results and sets the person in charge and deadline.

[0831] The "means for monitoring the progress of a task" is a function for continuously monitoring the progress of a generated task and updating it as necessary.

[0832] The "means for reminding" is a function that notifies the user that a task that should be addressed is incomplete when the deadline for the task approaches.

[0833] A "user interface" is an interactive screen that allows a user to view a task list, update task progress, and create new tasks.

[0834] "Means for monitoring in real time" refers to a function that constantly monitors the user's emotional state and immediately collects data if there is a change.

[0835] The "means for analyzing emotional data" is a technology that analyzes collected emotional data and determines the user's psychological state (e.g., joy, anger, sadness, surprise).

[0836] The "means for dynamically adjusting task priorities" is a function that appropriately changes the importance and urgency of tasks based on the user's emotional data, optimizing scheduling to reduce the user's burden.

[0837] The "means for adjusting the timing of reminders" is a function that adjusts the timing and frequency of reminders taking into account the user's emotional state, and notifies them at the optimal time.

[0838] MODE FOR CARRYING OUT THE INVENTION

[0839] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0840] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored as text files in a specific folder or database on the file system.

[0841] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology can be achieved using libraries such as NLTK or spaCy. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. Categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0842] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list and saved in the database.

[0843] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data. The analysis results are immediately transmitted to the server.

[0844] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. For example, one method is to send reminders when the user is relaxed, and conversely, to raise the threshold when the user is highly stressed.

[0845] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0846] Furthermore, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it can send a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0847] Specific examples

[0848] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0849] Prompt Sentence Examples

[0850] "A new request for quote has arrived. Please analyze the contents of this email and add it to your task list."

[0851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] The server periodically accesses the mail server to check for new emails. Specifically, it connects to the mail server using the IMAP or POP3 protocol and retrieves a list of new emails. The input is the connection information for the mail server, and the output is a list of new emails.

[0854] Step 2:

[0855] The server retrieves new emails and converts them to text format. This involves converting the email body to plain text format and extracting attachment information if any. The input is a list of new emails, and the output is email data in text format. Specifically, each email is saved as a text file.

[0856] Step 3:

[0857] The server reads the textual email data and parses it using natural language processing (NLP) techniques. It uses an NLP library (e.g., NLTK or spaCy) to extract important keywords and phrases from the email body. The input is the textual email data, and the output is the extracted keywords and phrases.

[0858] Step 4:

[0859] The server classifies the email content into specific categories based on the extracted keywords and phrases. Categories include "quote request," "inquiry," "order procedure," and "schedule adjustment." The input is the extracted keywords and phrases, and the output is category information. Specifically, the category is determined by checking against a predefined keyword list.

[0860] Step 5:

[0861] The server automatically generates a task list based on the classified category. An appropriate person in charge and a deadline are set for each task. The input is category information, and the output is a task list. For example, an email classified as "Request for quotation" generates a task called "Create a quotation," with a sales representative set as the person in charge and a deadline of three days after receipt.

[0862] Step 6:

[0863] The server saves the generated task list in a database. The input is the task list, and the output is the task information saved in the database. Specifically, the task list is inserted into an SQL database.

[0864] Step 7:

[0865] The device monitors the user's emotional state in real time and sends the collected data to the emotion engine. The input is camera footage and audio data, and the output is emotion data. Specifically, emotions are determined using facial recognition and voice analysis technology.

[0866] Step 8:

[0867] The server receives emotion data from the emotion engine and dynamically adjusts task priorities. The input is emotion data, and the output is a re-adjusted task list. For example, if a user feels very stressed, some tasks are postponed.

[0868] Step 9:

[0869] The server adjusts the timing of reminders based on the emotional data. The input is emotional data, and the output is the timing of reminders. For example, reminders can be sent when the user is relaxed, and can be withheld when the user is under stress.

[0870] Step 10:

[0871] The user terminal displays the generated task list through a user interface. The user checks the task list and updates the progress of each task through the interface. The input is the task list, and the output is the updated task list. For example, when the "Create a quote" task is completed, the task is marked as "Completed."

[0872] Step 11:

[0873] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is a task list, and the output is a reminder notification. For example, a reminder email saying "The quotation creation is incomplete" is sent to the user one day before the task deadline.

[0874] (Application example 2)

[0875] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0876] While conventional email analysis and task management systems effectively analyze received emails and generate tasks, they do not prioritize tasks or adjust them in real time based on the user's emotional state. This can lead to stress for specific users and imbalances in work loads, resulting in a loss of work efficiency and balance. Furthermore, in work environments such as factories, it is difficult to dynamically adjust tasks, resulting in insufficient optimization of collaboration between robots and humans. Therefore, a system that can grasp a user's emotional state in real time and assign tasks and send reminders at the appropriate time is needed.

[0877] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0878] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for recognizing the user's emotional state in real time, means for dynamically changing task priorities based on the user's emotional state, and means for displaying and updating the task list through a user interface. This allows the automatic adjustment of task priorities according to the user's emotional state, thereby enabling work efficiency and optimal balance.

[0879] "Means for receiving email" refers to a function that allows a server or terminal to automatically obtain new email from a mail server via a network such as the Internet.

[0880] "A means of analyzing received emails using natural language processing technology and classifying the email contents into specific categories" refers to a function that uses natural language processing (NLP) technology to extract important keywords and phrases from the email body, and then automatically classifies the email contents into specific categories, such as "request for quotation" or "inquiry," based on that.

[0881] "Means for automatically generating a task list based on the analyzed email content" is a function that automatically generates necessary tasks based on the analysis results and sets appropriate people in charge and deadlines.

[0882] The "means for monitoring task progress and sending reminders for tasks approaching their deadlines" is a function that constantly monitors the progress of the generated task list and sends reminders to the user when a task's deadline approaches.

[0883] "Means for recognizing the user's emotional state in real time" refers to a function that recognizes and grasps the user's emotional state (joy, anger, sadness, surprise, etc.) in real time by analyzing camera footage and audio data.

[0884] "Means for dynamically changing task priorities based on the user's emotional state" is a function that dynamically adjusts the priority and schedule of tasks in a task list based on the user's emotional data recognized in real time.

[0885] "Means for displaying and updating task lists through a user interface" is a function that displays the generated task list in an easy-to-understand manner to the user and provides an interface that allows the user to update the progress and completion status of tasks at any time.

[0886] This invention is a system for improving the efficiency of work management and task coordination in factories. This system automatically receives and analyzes emails, generates and manages tasks based on the content of the emails, and has the function of dynamically changing task priorities by recognizing the user's emotional state in real time.

[0887] First, the server receives emails from the factory manager. The server periodically accesses the mail server to retrieve new emails and converts them into text format. This email text is then saved in a file or database in an easy-to-parse format.

[0888] The server then analyzes the text of the saved email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body and automatically classifies the content into specific categories such as "quote request" or "inquiry." Based on this classification information, the server automatically generates a task list. An appropriate robot and deadline are assigned to each task. For example, if the email says "Please align the parts," a task called "align the parts" is generated, and the assigned robot is set to Robot A in the factory.

[0889] One of its distinctive features is that it incorporates an emotion engine that recognizes the user's emotions. The device analyzes camera footage and audio data to recognize the user's emotional state, such as joy, anger, sadness, or surprise, in real time. The recognized user emotion data is sent to the server and used to dynamically change task priorities. For example, if a line manager is feeling stressed, adjustments can be made, such as extending the deadline for a task. Also, if the user is feeling relaxed, measures can be taken, such as increasing the frequency of reminders.

[0890] The generated task list is displayed through a user interface. The user can check the progress of the tasks and update the progress of each task through the interface. For example, when a user completes the "align parts" task, the user can mark the task as "completed." The server monitors the progress of the tasks and sends reminders for tasks whose deadlines are approaching. Reminders are automatically sent before the task deadline, helping users to complete tasks without forgetting.

[0891] For illustrative purposes, consider the following prompt:

[0892] Analyze an email with the content "Please assign robot A to align the parts," generate appropriate tasks, and adjust the task priority taking into account the user's emotional state (stress, relaxation, etc.).

[0893] The hardware required to implement this system includes a server, mail server, client terminals (PCs and tablets), cameras, and microphones. The software includes Python, TextBlob, a mail analysis library, and an emotion recognition engine. This streamlines task management within the factory and enables flexible responses based on the user's emotional state.

[0894] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0895] Step 1:

[0896] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database. The input is new emails and the output is text format email data. This process involves retrieving emails and converting them into text format.

[0897] Step 2:

[0898] The server analyzes the text of the stored emails using natural language processing (NLP) techniques. The input is the email data in text format, and the output is extracted keywords and phrases. Specifically, it uses an NLP library such as TextBlob to extract important keywords and classify the content into specific categories.

[0899] Step 3:

[0900] The server automatically generates a task list based on the analysis results. The input is the keywords and categories resulting from the analysis, and the output is the task list. An appropriate robot and deadline are set for each task. Specifically, tasks such as "aligning parts" and "checking equipment" are defined based on the category, and a robot and deadline are assigned to each.

[0901] Step 4:

[0902] The device recognizes the user's emotional state in real time. The input is the user's camera video and audio data, and the output is the recognized emotional data. Using an emotion engine, the device analyzes the user's emotions, such as joy, anger, sadness, and surprise, and captures them as data.

[0903] Step 5:

[0904] The server dynamically adjusts task priorities based on the user's emotional data. The input is the emotional data and the task list, and the output is the adjusted task list. If the user is stressed, the deadline is extended, and if the user is relaxed, the frequency of reminders is increased.

[0905] Step 6:

[0906] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is the task list and the current time, and the output is a reminder notification. Specifically, a reminder email such as "The part alignment task is incomplete" is automatically sent one day before the deadline.

[0907] Step 7:

[0908] Users view and update their task lists through a user interface. The input is the task list and user actions, and the output is the updated task list. Using this interface, users can check the progress of tasks and mark completed tasks as "completed."

[0909] By following the above steps, it is possible to carry out everything from email analysis to task management, and even dynamic adjustment of tasks according to the user's emotions.

[0910] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0911] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0912] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0913] [Fourth embodiment]

[0914] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0915] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0916] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0917] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0918] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0919] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0920] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0921] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0922] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0923] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0924] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0925] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0926] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0927] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0928] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database.

[0929] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body, and classifies the email content into specific categories. Examples of categories used in this classification include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[0930] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate person and deadline. For example, in the case of a "quote request" email, a task called "Create a quote" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt.

[0931] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[0932] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[0933] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0934] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are ensured to be completed without being overlooked. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0935] The above is an embodiment of the present invention.

[0936] The processing flow will be explained below.

[0937] Step 1: Receiving email

[0938] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[0939] Step 2: Analyzing the email

[0940] The server analyzes the text of the stored emails using natural language processing technology. It extracts important keywords and phrases from the email body and classifies the content into specific categories. In this process, categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment" are used.

[0941] Step 3: Creating tasks

[0942] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote," sets the person in charge to a sales representative, and sets the deadline to three days after receipt. The generated task is added to the task list.

[0943] Step 4: View the task list

[0944] The terminal displays the task list to the user. The user can check the task list through the user interface. Details of each task, the person in charge, the deadline, and other information are displayed.

[0945] Step 5: Update the task

[0946] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[0947] Step 6: Remind

[0948] The server monitors the progress of the task list and sends reminders for tasks that are approaching their deadline. For example, a reminder email is sent to the user one day before the task deadline, saying, "The quotation creation is incomplete."

[0949] The above are the specific processing steps of the program of the present invention.

[0950] Example 1

[0951] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0952] Traditional email and task management systems required users to manually analyze email content and create and manage tasks, which was inefficient. Manually monitoring task progress and managing deadlines was also cumbersome, potentially resulting in work delays. Furthermore, there was a lack of consistency and accuracy in classifying tasks into specific categories, leading to the risk of important tasks being overlooked.

[0953] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0954] In this invention, the server includes means for receiving emails, means for analyzing received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for assigning appropriate personnel and deadlines to each task, means for monitoring the progress of tasks and sending reminders for tasks approaching deadlines, means for displaying and updating the task list through a user interface, and means for using a generative AI model in natural language processing technology. This enables automatic analysis of email content, highly accurate and consistent categorization, and task generation and management. Furthermore, the reminder function ensures thorough deadline management and prevents work delays.

[0955] "Means for receiving email" refers to a device or software that has the function of periodically receiving new email from a mail server.

[0956] "Natural language processing technology" refers to methods and technologies for analyzing natural language and extracting meaning from text data, and examples include NLP libraries and models.

[0957] A "means for classifying into specific categories" is a device or software that has the function of analyzing the content of received emails and classifying them into preset categories such as "request for quotation," "inquiry," "order procedure," and "schedule adjustment."

[0958] The "means for automatically generating a task list" refers to a device or software that has the function of listing the necessary tasks based on the analyzed email content, and allowing the system to automatically create the task list.

[0959] The "means for setting an appropriate person in charge and a deadline" is a device or software that has the function of assigning an appropriate person in charge to a generated task and setting a corresponding deadline.

[0960] The "means for monitoring the progress of a task" refers to a device or software that has the function of continuously monitoring the progress of a generated task and grasping the current status.

[0961] The "means for sending reminders" refers to a device or software that has the function of sending reminder notifications to the person in charge when the deadline for a task approaches.

[0962] The "means for displaying and updating a task list through a user interface" refers to a device or software that displays a task list on a user terminal and allows the user to change or update the progress status of tasks through the interface.

[0963] A "generative AI model" is an artificial intelligence model that realizes various data analysis and generation functions, including natural language processing, and specific examples include models such as GPT and BERT.

[0964] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0965] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze. This conversion and saving process uses, for example, Python's IMAP library, and saves the received emails in a local text file or database.

[0966] The server then analyzes the text of the stored emails using natural language processing (NLP) techniques, such as the NLTK library or spaCy. These tools extract important keywords and phrases from the email body, and based on the results, classify the emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment." Generative AI models (such as GPT and BERT) are also applied to improve the accuracy of the analysis.

[0967] Based on the analysis results, the server automatically generates a task list. This task list is generated according to pre-set rules. For example, in the case of a "quote request" email, a task called "Create a quote" is automatically generated, assigned to a sales representative, and the due date is set to three days after receipt.

[0968] The generated task list is displayed on the user's device through a user interface. The user interface can be a front-end application built using React or Vue.js, for example. Through this interface, the user can check the task list and update the progress of each task. For example, when a user completes the "Create a quote" task, they can mark the task as "Complete."

[0969] The server also monitors the progress of tasks and sends reminders for tasks that are approaching their deadlines. For example, an email sending script is used to send a reminder email to the user one day before the task is due, saying "Your quotation is not yet complete."

[0970] Examples and prompts

[0971] Specific examples

[0972] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[0973] Prompt Sentence Examples

[0974] "Receive a new email and classify it into a category called 'Request for quote' based on its content. Next, create a task called 'Create quote' and set the assignee and deadline. Then, display the created task in the user interface."

[0975] This invention integrates email management and task management, automatically analyzes email content, and converts it into appropriate tasks, ensuring that important tasks are not overlooked and are carried out reliably. In addition, the reminder function ensures thorough deadline management, preventing work delays.

[0976] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0977] Step 1:

[0978] The server accesses the mail server to check for new mail and receives it.

[0979] Input: Request to access mail server

[0980] Data processing: Retrieve new emails from the mail server using the IMAP or POP3 protocol.

[0981] Output: Retrieved email data

[0982] Specific operation: The server uses Python's IMAP library to connect to the mail server and retrieve new emails. The retrieved emails are temporarily stored in memory.

[0983] Step 2:

[0984] The server converts the received email into text format and stores it in a format that is easy to analyze.

[0985] Input: Received email data

[0986] Data processing: Analyzes the HTML and MIME structure of the email and extracts the text portion.

[0987] Output: Email data converted to text format

[0988] What it does: The server extracts the text of the email and saves it to a local text file or database, along with metadata such as the sender and the date and time the email was received.

[0989] Step 3:

[0990] The server analyzes the text of the stored emails using natural language processing (NLP) techniques.

[0991] Input: Email data in text format

[0992] Data processing: Using inductive analysis techniques, important keywords and phrases are extracted.

[0993] Output: Extracted keywords and phrases

[0994] What it does: The server uses the NLTK library and spaCy to parse the text and extract key keywords and phrases. Optionally, it also leverages generative AI models (e.g., GPT and BERT) to improve the accuracy of the analysis.

[0995] Step 4:

[0996] The server classifies emails into specific categories based on the extracted keywords and phrases.

[0997] Input: Extracted keywords and phrases

[0998] Data crunching: Applying classification algorithms using keyword matching and machine learning models.

[0999] Output: Categorized email data

[1000] How it works: The server uses SciKit-Learn's clustering algorithm to classify emails into categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment."

[1001] Step 5:

[1002] The server automatically generates a task list based on the analysis results.

[1003] Input: Categorized email data

[1004] Data processing: Convert into task list format and set the necessary information (task name, person in charge, deadline, etc.).

[1005] Output: Generated task list

[1006] Specific operation: The server generates tasks according to the category of the email based on pre-defined rules. For example, if an email is a "quote request," a task called "Create a quote" is automatically generated, and a person in charge and deadline are set.

[1007] Step 6:

[1008] The server transmits the generated task list to the user terminal.

[1009] Input: Generated task list

[1010] Data processing: Convert the task list into an interface format.

[1011] Output: Task list data sent to the user's terminal

[1012] Specific operation: The server acts as the backend system of the web application, retrieving the generated task list from the database and sending it to the frontend.

[1013] Step 7:

[1014] The user terminal displays the task list through a user interface.

[1015] Input: Task list data sent from the server

[1016] Data processing: Convert the task list into a display format

[1017] Output: Task list displayed on the user interface

[1018] Specific operation: The user device displays a task list in dashboard format on a front end built using React and Vue.js.

[1019] Step 8:

[1020] The user can view the task list and update the progress of each task through the interface.

[1021] Input: Task list on the user interface

[1022] Data manipulation: Updating task progress

[1023] Output: Updated task list

[1024] How it works: Users can change the status of each task to "in progress" or "completed" in the interface, and that information is updated in the database in real time.

[1025] Step 9:

[1026] The server monitors the progress of tasks and provides reminders for tasks approaching deadlines.

[1027] Input: Task list and progress information in a database

[1028] Data calculation: Check deadlines and extract tasks that need reminders

[1029] Output: Send reminder email

[1030] What it does: The server uses a periodic job (e.g. a Cron job) to check the task progress and deadline information from the database, and generates and sends reminder emails to users.

[1031] (Application example 1)

[1032] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1033] In logistics centers, tasks requested via email (e.g., shipping preparation, inventory checks, inspection work, etc.) are often managed manually. This results in tasks being overlooked or delayed, leading to problems with reduced work efficiency. Furthermore, there is a lack of real-time feedback that allows employees to work efficiently. To resolve these operational inefficiencies, a system is needed that can automatically analyze the content of emails and generate and manage tasks based on that analysis.

[1034] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1035] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email content into specific categories, means for automatically generating a task list based on the analyzed email content, means for monitoring the progress of tasks and sending reminders for tasks with approaching deadlines, means for displaying and updating the task list through a user interface, and means for displaying the task list on a smart device and providing visual feedback on the update status in real time. This enables automation of task management in logistics centers, preventing task oversights and delays and significantly improving work efficiency. By allowing employees to work while checking the progress of tasks in real time on their smart devices, work efficiency is improved and errors are reduced.

[1036] "Means for receiving email" refers to a device or system that has the function of periodically receiving new emails from an email server.

[1037] "Means for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories" refers to a device or system that analyzes the text data of received emails, analyzes the content using natural language processing technology, and classifies the content into appropriate categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment, shipping preparation, inventory check, inspection work, etc.).

[1038] The "means for automatically generating a task list based on the analyzed email content" refers to a device or system that has the function of automatically generating corresponding tasks in list form based on the results of email analysis.

[1039] A "means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching" is a device or system that has the function of monitoring the progress of each task included in a task list in real time and sending reminder notifications when the deadlines are approaching.

[1040] A "means for displaying and updating a task list through a user interface" is a device or system that provides an interface that allows a user to visually view a task list and update the status of each task.

[1041] "Means for displaying a task list on a smart device and providing real-time visual feedback on the update status" refers to a device or system that has the function of displaying a task list on smart glasses or other portable digital devices and providing real-time visual feedback on the progress of tasks.

[1042] The present invention is a system designed for efficient task management in a logistics center. This system has the function of automatically analyzing received emails and generating and managing a task list based on the analysis. The following describes in detail an embodiment of the present invention.

[1043] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a format that is easy to analyze, such as a text file or a database. The server then analyzes the saved email text using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body. As a result, the email content is classified into specific categories. Categories used in this classification process include "quote request," "inquiry," "order procedure," "schedule adjustment," "shipment preparation," "inventory check," and "inspection work."

[1044] Based on the analysis results, the server automatically generates a task list. Each task is assigned an appropriate assignee and deadline. For example, for a "Prepare for shipment" email, a task called "Prepare for shipment" is generated, with the assignee set to logistics staff and the deadline set to three days after receipt. The generated task list is displayed through a user interface on the smart device (e.g., smart glasses). The user can check the task list and update the progress of each task through the interface. For example, when a user completes the "Prepare for shipment" task, they can mark the task as "Completed."

[1045] The server also monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, a reminder notification stating "Shipping preparation work is not yet complete" can be sent to smart devices one day before the task deadline. Employees can work while checking the progress of their tasks in real time, improving work efficiency and reducing errors.

[1046] This system is implemented using the following hardware and software. The hardware includes smart glasses and a server. The software includes Python, imaplib, email, sklearn, and spacy. The server uses this software to receive emails, analyze text, generate tasks, and send reminders. The smart glasses display a task list in real time and accept input from the user.

[1047] As a concrete example, if a logistics center manager receives an email saying, "Please prepare for shipment," the system automatically analyzes the email's contents, generates a task called "prepare for shipment," and displays it on the smart glasses. Employees can then confirm the task, prepare for shipment, and report completion to the system through the smart glasses. This automates task management and enables efficient business operations.

[1048] Example prompt sentence:

[1049] "Please prepare shipment for new orders."

[1050] "Please check stock availability"

[1051] "Please inspect the new shipping list."

[1052] The above is an embodiment of the present invention.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] The server periodically accesses the mail server and receives new mail.

[1056] Specifically, the server connects to the mail server using the IMAP protocol and searches for "unread mail," resulting in a list of new mail.

[1057] Input: Mail server account information, IMAP protocol

[1058] Output: List of new emails

[1059] Step 2:

[1060] The server converts the received email into text format and saves it in a format that is easy to analyze.

[1061] Specifically, the email body is extracted and saved in a text file or database.

[1062] Input: List of new emails

[1063] Output: Plain text of the email body (saved file or database entry)

[1064] Step 3:

[1065] The server analyzes the text of stored emails using natural language processing (NLP) techniques.

[1066] Specifically, the server uses the spacy library to parse the email body, extract important keywords and phrases, and classify the content into specific categories.

[1067] Input: Email body in plain text format

[1068] Output: Important keywords and phrases, categorization results

[1069] Step 4:

[1070] The server automatically generates a task list based on the parsed email content.

[1071] Specifically, the server generates appropriate tasks based on keywords and categories, and assigns a person in charge and a deadline to each task.

[1072] Input: Category classification results, keywords

[1073] Output: Auto-generated task list (task name, assignee, due date)

[1074] Step 5:

[1075] The server displays the task list on the smart device and provides visual feedback on the update status in real time.

[1076] Specifically, the server sends the task list to a smart device (e.g., smart glasses) so that the user can check it. When the task progress is updated, the information is displayed in real time.

[1077] Input: Task list

[1078] Output: Task list display on smart device

[1079] Step 6:

[1080] The user views the task list and updates the progress of each task through the interface.

[1081] Specifically, the user updates the task completion status through the smart device, and the updated information is sent to the server, which then updates the task list again.

[1082] Input: User task progress update

[1083] Output: Updated task list

[1084] Step 7:

[1085] The server monitors the progress of tasks and reminds you about tasks that are approaching their deadlines.

[1086] Specifically, the server monitors the deadline for each task in the task list, and when the deadline approaches, it generates a reminder notification and sends it to the smart device.

[1087] Input: Due date for each task in the task list

[1088] Output: Remind notification (notification to smart device)

[1089] The above are the specific processing steps of this system.

[1090] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1091] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[1092] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored in a text file or database.

[1093] The server then analyzes the text of the stored email using natural language processing (NLP) technology, which extracts important keywords and phrases from the email body and classifies the content into specific categories. Examples of categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[1094] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and a deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list.

[1095] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data.

[1096] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. One method is to send reminders when the user is relaxed, or, conversely, to raise the threshold when the user is highly stressed.

[1097] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[1098] In addition, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it sends a reminder email to the user one day before the task deadline saying, "Your quotation is not yet complete."

[1099] As a concrete example, let's say a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and a deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[1100] This invention integrates email management and task management, improving work efficiency. By automatically analyzing email content and converting it into appropriate tasks, important tasks are not overlooked and are reliably progressed. Furthermore, by adding a user emotion recognition function using an emotion engine, task priorities are adjusted and reminders are set according to the user's state, thereby reducing workload imbalances and stress.

[1101] The above is an embodiment of the present invention.

[1102] The processing flow will be explained below.

[1103] Step 1: Receiving email

[1104] The server periodically accesses the mail server to check for new emails. If new emails are found, they are received and converted into text format. The converted email content is then saved in a file or database in an easy-to-analyze format.

[1105] Step 2: Analyzing the email

[1106] The server analyzes the text of the stored emails using natural language processing technology. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. In this classification process, categories such as "quote request," "inquiry," "order procedure," and "schedule adjustment" are used.

[1107] Step 3: Creating tasks

[1108] The server automatically generates a task list based on the parsed email content. For example, for a "quote request" email, it generates a task called "Create a quote." The person in charge is set to a sales representative, and the deadline is set to three days after receipt. The generated task is added to the task list.

[1109] Step 4: Receiving emotion data

[1110] The device collects data from the camera and microphone to monitor the user's emotional state in real time. The emotion engine analyzes this data and recognizes the user's emotional state (happiness, anger, surprise, sadness, etc.). The results are sent to the server.

[1111] Step 5: Adjust task priorities

[1112] The server dynamically adjusts task priorities based on the received emotional data: for example, if the user is feeling stressed, it postpones complex tasks and prioritizes simpler ones.

[1113] Step 6: View the task list

[1114] The terminal displays the task list to the user through a user interface, and the user can check the task list and see information such as the details of each task, the person in charge, the deadline, and the priority.

[1115] Step 7: Update the task

[1116] Users update the progress of tasks in the task list, for example, when they complete "Create a quote," they mark the task as "Complete," and the updates are sent to the server, keeping the task list up to date.

[1117] Step 8: Remind

[1118] The server monitors the progress of the task list and sends reminders for tasks approaching their deadlines. Based on emotional data, it can send reminders when the user is relaxed, or adjust the timing when the user is under stress. For example, it can send a reminder email or display a pop-up notification one day before the task is due, saying, "Your quotation is incomplete."

[1119] The above are the specific processing steps of the present invention.

[1120] Example 2

[1121] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1122] While conventional email and task management systems are capable of receiving emails and generating tasks based on them, they lack the functionality to dynamically change task priorities while taking into account the user's emotional state. This makes it difficult to appropriately manage users' stress and workload, and has led to issues such as insufficient improvements in work efficiency and reduction of psychological burden.

[1123] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1124] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for displaying and updating the task list through a user interface, means for monitoring the user's emotional state in real time and analyzing emotional data, means for dynamically adjusting task priorities based on the emotional data, and means for adjusting the timing of reminders based on the emotional data. This not only integrates email management and task management, but also dynamically adjusts task priorities and the timing of reminders according to the user's emotional state, thereby improving work efficiency and reducing psychological burden.

[1125] "Means for receiving email" is a function that allows the server to access the email server, obtain new emails, and save them in an analyzable format.

[1126] "Natural language processing technology" is a technology that analyzes grammatical and semantic information from text data and extracts necessary keywords and phrases.

[1127] "Means for classifying into specific categories" refers to a function that automatically sorts the analyzed email content into predefined categories (e.g., request for quotation, inquiry, order procedure, schedule adjustment).

[1128] "Means for automatically generating task lists" is a function that generates new tasks that need to be addressed based on the analysis results and sets the person in charge and deadline.

[1129] The "means for monitoring the progress of a task" is a function for continuously monitoring the progress of a generated task and updating it as necessary.

[1130] The "means for reminding" is a function that notifies the user that a task that should be addressed is incomplete when the deadline for the task approaches.

[1131] A "user interface" is an interactive screen that allows a user to view a task list, update task progress, and create new tasks.

[1132] "Means for monitoring in real time" refers to a function that constantly monitors the user's emotional state and immediately collects data if there is a change.

[1133] The "means for analyzing emotional data" is a technology that analyzes collected emotional data and determines the user's psychological state (e.g., joy, anger, sadness, surprise).

[1134] The "means for dynamically adjusting task priorities" is a function that appropriately changes the importance and urgency of tasks based on the user's emotional data, optimizing scheduling to reduce the user's burden.

[1135] The "means for adjusting the timing of reminders" is a function that adjusts the timing and frequency of reminders taking into account the user's emotional state, and notifies them at the optimal time.

[1136] MODE FOR CARRYING OUT THE INVENTION

[1137] The present invention is a system that automatically receives and analyzes emails, and generates and manages tasks based on the content of the emails, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[1138] First, the server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database in an easy-to-analyze format. For example, they are stored as text files in a specific folder or database on the file system.

[1139] The server then analyzes the text of the stored email using natural language processing (NLP) technology. NLP technology can be achieved using libraries such as NLTK or spaCy. NLP technology extracts important keywords and phrases from the email body and classifies the content into specific categories. Categories used in this classification process include "request for quote," "inquiry," "order processing," and "schedule adjustment."

[1140] Based on the analysis results, the server automatically generates a task list. An appropriate person in charge and deadline are set for each task. For example, in the case of a "Request for Quotation" email, a task called "Create a quotation" is generated, with the person in charge set to a sales representative and the deadline set to three days after receipt. The generated task is added to the task list and saved in the database.

[1141] A distinctive feature of this invention is the incorporation of an emotion engine that recognizes the user's emotions. The device monitors the user's emotional state in real time and transmits the data to the server via the emotion engine. The emotion engine recognizes the user's emotions, such as joy, anger, sadness, and surprise, by analyzing, for example, camera footage and audio data. The analysis results are immediately transmitted to the server.

[1142] The server dynamically changes task priorities based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, task scheduling is performed to reduce the task load. The server also adjusts the method and timing of reminders based on the emotional data. For example, one method is to send reminders when the user is relaxed, and conversely, to raise the threshold when the user is highly stressed.

[1143] The generated task list is displayed on the user's terminal through a user interface. The user can check the task list and update the progress of each task through the interface. For example, when the user completes the "Create a quote" task, the user can mark the task as "Complete."

[1144] Furthermore, the server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. For example, it can send a reminder email to the user one day before the task deadline saying, "The quotation creation is incomplete."

[1145] Specific examples

[1146] Suppose a sales representative receives a new email. The email's content is a request for a quote. The server receives the email and converts it into text format. Next, natural language processing technology analyzes the email content and classifies it into a category called "quote request." Based on the analysis results, a task called "create a quote" is generated, and a person in charge and deadline are assigned. The task list is then displayed on the user's device. The sales representative checks the task list and creates a quote within the deadline to complete the task.

[1147] Prompt Sentence Examples

[1148] "A new request for quote has arrived. Please analyze the contents of this email and add it to your task list."

[1149] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] The server periodically accesses the mail server to check for new emails. Specifically, it connects to the mail server using the IMAP or POP3 protocol and retrieves a list of new emails. The input is the connection information for the mail server, and the output is a list of new emails.

[1152] Step 2:

[1153] The server retrieves new emails and converts them to text format. This involves converting the email body to plain text format and extracting attachment information if any. The input is a list of new emails, and the output is email data in text format. Specifically, each email is saved as a text file.

[1154] Step 3:

[1155] The server reads the textual email data and parses it using natural language processing (NLP) techniques. It uses an NLP library (e.g., NLTK or spaCy) to extract important keywords and phrases from the email body. The input is the textual email data, and the output is the extracted keywords and phrases.

[1156] Step 4:

[1157] The server classifies the email content into specific categories based on the extracted keywords and phrases. Categories include "quote request," "inquiry," "order procedure," and "schedule adjustment." The input is the extracted keywords and phrases, and the output is category information. Specifically, the category is determined by checking against a predefined keyword list.

[1158] Step 5:

[1159] The server automatically generates a task list based on the classified category. An appropriate person in charge and a deadline are set for each task. The input is category information, and the output is a task list. For example, an email classified as "Request for quotation" generates a task called "Create a quotation," with a sales representative set as the person in charge and a deadline of three days after receipt.

[1160] Step 6:

[1161] The server saves the generated task list in a database. The input is the task list, and the output is the task information saved in the database. Specifically, the task list is inserted into an SQL database.

[1162] Step 7:

[1163] The device monitors the user's emotional state in real time and sends the collected data to the emotion engine. The input is camera footage and audio data, and the output is emotion data. Specifically, emotions are determined using facial recognition and voice analysis technology.

[1164] Step 8:

[1165] The server receives emotion data from the emotion engine and dynamically adjusts task priorities. The input is emotion data, and the output is a re-adjusted task list. For example, if a user feels very stressed, some tasks are postponed.

[1166] Step 9:

[1167] The server adjusts the timing of reminders based on the emotional data. The input is emotional data, and the output is the timing of reminders. For example, reminders can be sent when the user is relaxed, and can be withheld when the user is under stress.

[1168] Step 10:

[1169] The user terminal displays the generated task list through a user interface. The user checks the task list and updates the progress of each task through the interface. The input is the task list, and the output is the updated task list. For example, when the "Create a quote" task is completed, the task is marked as "Completed."

[1170] Step 11:

[1171] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is a task list, and the output is a reminder notification. For example, a reminder email saying "The quotation creation is incomplete" is sent to the user one day before the task deadline.

[1172] (Application example 2)

[1173] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] While conventional email analysis and task management systems effectively analyze received emails and generate tasks, they do not prioritize tasks or adjust them in real time based on the user's emotional state. This can lead to stress for specific users and imbalances in work loads, resulting in a loss of work efficiency and balance. Furthermore, in work environments such as factories, it is difficult to dynamically adjust tasks, resulting in insufficient optimization of collaboration between robots and humans. Therefore, a system that can grasp a user's emotional state in real time and assign tasks and send reminders at the appropriate time is needed.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1176] In this invention, the server includes means for receiving emails, means for analyzing the received emails using natural language processing technology and classifying the email contents into specific categories, means for automatically generating a task list based on the analyzed email contents, means for monitoring the progress of tasks and sending reminders for tasks whose deadlines are approaching, means for recognizing the user's emotional state in real time, means for dynamically changing task priorities based on the user's emotional state, and means for displaying and updating the task list through a user interface. This allows the automatic adjustment of task priorities according to the user's emotional state, thereby enabling work efficiency and optimal balance.

[1177] "Means for receiving email" refers to a function that allows a server or terminal to automatically obtain new email from a mail server via a network such as the Internet.

[1178] "A means of analyzing received emails using natural language processing technology and classifying the email contents into specific categories" refers to a function that uses natural language processing (NLP) technology to extract important keywords and phrases from the email body, and then automatically classifies the email contents into specific categories, such as "request for quotation" or "inquiry," based on that.

[1179] "Means for automatically generating a task list based on the analyzed email content" is a function that automatically generates necessary tasks based on the analysis results and sets appropriate people in charge and deadlines.

[1180] The "means for monitoring task progress and sending reminders for tasks approaching their deadlines" is a function that constantly monitors the progress of the generated task list and sends reminders to the user when a task's deadline approaches.

[1181] "Means for recognizing the user's emotional state in real time" refers to a function that recognizes and grasps the user's emotional state (joy, anger, sadness, surprise, etc.) in real time by analyzing camera footage and audio data.

[1182] "Means for dynamically changing task priorities based on the user's emotional state" is a function that dynamically adjusts the priority and schedule of tasks in a task list based on the user's emotional data recognized in real time.

[1183] "Means for displaying and updating task lists through a user interface" is a function that displays the generated task list in an easy-to-understand manner to the user and provides an interface that allows the user to update the progress and completion status of tasks at any time.

[1184] This invention is a system for improving the efficiency of work management and task coordination in factories. This system automatically receives and analyzes emails, generates and manages tasks based on the content of the emails, and has the function of dynamically changing task priorities by recognizing the user's emotional state in real time.

[1185] First, the server receives emails from the factory manager. The server periodically accesses the mail server to retrieve new emails and converts them into text format. This email text is then saved in a file or database in an easy-to-parse format.

[1186] The server then analyzes the text of the saved email using natural language processing (NLP) technology. NLP technology extracts important keywords and phrases from the email body and automatically classifies the content into specific categories such as "quote request" or "inquiry." Based on this classification information, the server automatically generates a task list. An appropriate robot and deadline are assigned to each task. For example, if the email says "Please align the parts," a task called "align the parts" is generated, and the assigned robot is set to Robot A in the factory.

[1187] One of its distinctive features is that it incorporates an emotion engine that recognizes the user's emotions. The device analyzes camera footage and audio data to recognize the user's emotional state, such as joy, anger, sadness, or surprise, in real time. The recognized user emotion data is sent to the server and used to dynamically change task priorities. For example, if a line manager is feeling stressed, adjustments can be made, such as extending the deadline for a task. Also, if the user is feeling relaxed, measures can be taken, such as increasing the frequency of reminders.

[1188] The generated task list is displayed through a user interface. The user can check the progress of the tasks and update the progress of each task through the interface. For example, when a user completes the "align parts" task, the user can mark the task as "completed." The server monitors the progress of the tasks and sends reminders for tasks whose deadlines are approaching. Reminders are automatically sent before the task deadline, helping users to complete tasks without forgetting.

[1189] For illustrative purposes, consider the following prompt:

[1190] Analyze an email with the content "Please assign robot A to align the parts," generate appropriate tasks, and adjust the task priority taking into account the user's emotional state (stress, relaxation, etc.).

[1191] The hardware required to implement this system includes a server, mail server, client terminals (PCs and tablets), cameras, and microphones. The software includes Python, TextBlob, a mail analysis library, and an emotion recognition engine. This streamlines task management within the factory and enables flexible responses based on the user's emotional state.

[1192] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1193] Step 1:

[1194] The server periodically accesses the mail server to receive new emails. The received emails are converted into text format and saved in a file or database. The input is new emails and the output is text format email data. This process involves retrieving emails and converting them into text format.

[1195] Step 2:

[1196] The server analyzes the text of the stored emails using natural language processing (NLP) techniques. The input is the email data in text format, and the output is extracted keywords and phrases. Specifically, it uses an NLP library such as TextBlob to extract important keywords and classify the content into specific categories.

[1197] Step 3:

[1198] The server automatically generates a task list based on the analysis results. The input is the keywords and categories resulting from the analysis, and the output is the task list. An appropriate robot and deadline are set for each task. Specifically, tasks such as "aligning parts" and "checking equipment" are defined based on the category, and a robot and deadline are assigned to each.

[1199] Step 4:

[1200] The device recognizes the user's emotional state in real time. The input is the user's camera video and audio data, and the output is the recognized emotional data. Using an emotion engine, the device analyzes the user's emotions, such as joy, anger, sadness, and surprise, and captures them as data.

[1201] Step 5:

[1202] The server dynamically adjusts task priorities based on the user's emotional data. The input is the emotional data and the task list, and the output is the adjusted task list. If the user is stressed, the deadline is extended, and if the user is relaxed, the frequency of reminders is increased.

[1203] Step 6:

[1204] The server monitors the progress of tasks and sends reminders for tasks approaching their deadlines. The input is the task list and the current time, and the output is a reminder notification. Specifically, a reminder email such as "The part alignment task is incomplete" is automatically sent one day before the deadline.

[1205] Step 7:

[1206] Users view and update their task lists through a user interface. The input is the task list and user actions, and the output is the updated task list. Using this interface, users can check the progress of tasks and mark completed tasks as "completed."

[1207] By following the above steps, it is possible to carry out everything from email analysis to task management, and even dynamic adjustment of tasks according to the user's emotions.

[1208] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1209] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1210] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1211] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1212] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1213] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1214] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1215] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1216] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1217] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1218] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1219] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1220] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1221] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1222] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1223] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1224] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1225] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1226] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1227] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1228] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1229] The following is further disclosed regarding the above embodiment.

[1230] (Claim 1)

[1231] A means for receiving email;

[1232] A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories;

[1233] A means for automatically generating a task list based on the analyzed email content;

[1234] A way to monitor task progress and receive reminders about tasks that are approaching deadlines,

[1235] means for displaying and updating the task list through a user interface;

[1236] A system including:

[1237] (Claim 2)

[1238] 2. The system according to claim 1, further comprising means for setting a person in charge and a deadline based on the analysis result of the received email.

[1239] (Claim 3)

[1240] 2. The system according to claim 1, further comprising means for classifying into categories such as quotation requests, inquiries, order procedures, and schedule adjustments when classifying categories based on the analysis results.

[1241] "Example 1"

[1242] (Claim 1)

[1243] A means for receiving email;

[1244] A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories;

[1245] A means for automatically generating a task list based on the analyzed email content;

[1246] A way to assign the right person and deadline for each task,

[1247] A way to monitor task progress and receive reminders about tasks that are approaching deadlines,

[1248] means for displaying and updating the task list through a user interface;

[1249] A system including:

[1250] (Claim 2)

[1251] 2. The system according to claim 1, further comprising means for classifying received emails into categories such as requests for quotes, inquiries, order procedures, and schedule adjustments when classifying the received emails based on the analysis results of the emails.

[1252] (Claim 3)

[1253] 10. The system of claim 1, further comprising means for employing generative AI models in natural language processing techniques.

[1254] "Application Example 1"

[1255] (Claim 1)

[1256] A means for receiving email;

[1257] A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories;

[1258] A means for automatically generating a task list based on the analyzed email content;

[1259] A way to monitor task progress and receive reminders about tasks that are approaching deadlines,

[1260] means for displaying and updating the task list through a user interface;

[1261] A way to display task lists on smart devices and provide visual feedback on update status in real time,

[1262] A system including:

[1263] (Claim 2)

[1264] 2. The system according to claim 1, further comprising means for setting a person in charge and a deadline based on the analysis result of the received email.

[1265] (Claim 3)

[1266] The system according to claim 1, further comprising means for classifying into categories such as quotation requests, inquiries, order procedures, schedule adjustments, or shipping preparations, inventory checks, and inspection work when classifying categories based on the analysis results.

[1267] "Example 2: Combining Emotion Engines"

[1268] (Claim 1)

[1269] A means for receiving email;

[1270] A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories;

[1271] A means for automatically generating a task list based on the analyzed email content;

[1272] A way to monitor task progress and receive reminders about tasks that are approaching deadlines,

[1273] means for displaying and updating the task list through a user interface;

[1274] means for monitoring the user's emotional state in real time and analyzing the emotional data;

[1275] a means for dynamically adjusting task priorities based on the emotion data;

[1276] A means for adjusting the timing of reminders based on emotional data;

[1277] A system including:

[1278] (Claim 2)

[1279] 2. The system according to claim 1, further comprising means for setting a person in charge and a deadline based on the analysis result of the received email.

[1280] (Claim 3)

[1281] 2. The system according to claim 1, further comprising means for classifying into categories such as quotation requests, inquiries, order procedures, and schedule adjustments when classifying categories based on the analysis results.

[1282] "Application example 2 when combining emotion engines"

[1283] (Claim 1)

[1284] A means for receiving email;

[1285] A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories;

[1286] A means for automatically generating a task list based on the analyzed email content;

[1287] A way to monitor task progress and receive reminders about tasks that are approaching deadlines,

[1288] means for recognizing a user's emotional state in real time;

[1289] means for dynamically changing task priorities based on the user's emotional state;

[1290] means for displaying and updating the task list through a user interface;

[1291] A system including:

[1292] (Claim 2)

[1293] 2. The system according to claim 1, further comprising means for setting a person in charge and a deadline based on the analysis result of the received email.

[1294] (Claim 3)

[1295] 2. The system according to claim 1, further comprising means for classifying into categories such as quotation requests, inquiries, order procedures, and schedule adjustments when classifying categories based on the analysis results. [Explanation of symbols]

[1296] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving email; A method for analyzing received emails using natural language processing technology and classifying the content of the emails into specific categories; A means for automatically generating a task list based on the analyzed email content; A way to monitor task progress and receive reminders about tasks that are approaching deadlines, means for displaying and updating the task list through a user interface; A system including:

2. 2. The system according to claim 1, further comprising means for setting a person in charge and a deadline based on the analysis result of the received email.

3. 2. The system according to claim 1, further comprising means for classifying into categories such as quotation requests, inquiries, order procedures, and schedule adjustments when classifying into categories based on the analysis results.

Images